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<a href="_c_svm_trainer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_CSVMTRAINER_H</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="preprocessor">#define SHARK_ALGORITHMS_CSVMTRAINER_H</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span> </div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span> </div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_svm_trainer_8h.html">shark/Algorithms/Trainers/AbstractSvmTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_weighted_trainer_8h.html">shark/Algorithms/Trainers/AbstractWeightedTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;<a class="code" href="_box_constrained_problems_8h.html">shark/Algorithms/QP/BoxConstrainedProblems.h</a>&gt;</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &lt;<a class="code" href="_svm_problems_8h.html">shark/Algorithms/QP/SvmProblems.h</a>&gt;</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &lt;<a class="code" href="_qp_box_linear_8h.html">shark/Algorithms/QP/QpBoxLinear.h</a>&gt;</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &lt;<a class="code" href="_cached_matrix_8h.html">shark/LinAlg/CachedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &lt;<a class="code" href="_gaussian_kernel_matrix_8h.html">shark/LinAlg/GaussianKernelMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_matrix_8h.html">shark/LinAlg/KernelMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="preprocessor">#include &lt;<a class="code" href="_precomputed_matrix_8h.html">shark/LinAlg/PrecomputedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="preprocessor">#include &lt;<a class="code" href="_regularized_kernel_matrix_8h.html">shark/LinAlg/RegularizedKernelMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="preprocessor">#include &lt;<a class="code" href="_gaussian_rbf_kernel_8h.html">shark/Models/Kernels/GaussianRbfKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span> </div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment">//for MCSVMs!</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="preprocessor">#include &lt;<a class="code" href="_qp_mc_simplex_decomp_8h.html">shark/Algorithms/QP/QpMcSimplexDecomp.h</a>&gt;</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="preprocessor">#include &lt;<a class="code" href="_qp_mc_box_decomp_8h.html">shark/Algorithms/QP/QpMcBoxDecomp.h</a>&gt;</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="preprocessor">#include &lt;<a class="code" href="_qp_mc_linear_8h.html">shark/Algorithms/QP/QpMcLinear.h</a>&gt;</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment">//~ #include &lt;shark/Algorithms/Trainers/McSvm/McSvmMMRTrainer.h&gt;</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment">//~ #include &lt;shark/Algorithms/Trainers/McSvm/McReinforcedSvmTrainer.h&gt;</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span> </div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>    </div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>    </div>
<div class="foldopen" id="foldopen00027" data-start="{" data-end="};">
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno"><a class="line" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">   27</a></span><span class="keyword">enum class</span> <a class="code hl_enumeration" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">McSvm</a>{</div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a0a9b52fb6605edc74fd7d5359f34477e">WW</a>,</div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a8d7e99c73cd5a10adaaf4c9f9a520368">CS</a>,</div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a69120030cbf53ae8224b9b4865ab3945">LLW</a>,</div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a292f535a3b6fe8853df2f03c8ed890a1">ATM</a>,</div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a600d2c32c47d0285e1df97492ed3bd35">ATS</a>,</div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a6fb4f22992a0d164b77267fde5477248">ADM</a>,</div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a586ba478b7ebec665f3df120799b6c2e">OVA</a>,</div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112ada27331778aba3d85176f2c76f49bcc8">MMR</a>,</div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>    <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a354624ba01e474ac153a71c5e0d3b266">ReinforcedSvm</a></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>};</div>
</div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment"></span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">///</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">/// \brief Training of C-SVMs for binary classification.</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">///</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">/// The C-SVM is the &quot;standard&quot; support vector machine for</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// binary (two-class) classification. Given are data tuples</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// \f$ (x_i, y_i) \f$ with x-component denoting input and</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// y-component denoting the label +1 or -1 (see the tutorial on</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// label conventions; the implementation uses values 0/1),</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// a kernel function k(x, x&#39;) and a regularization</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// constant C &gt; 0. Let H denote the kernel induced</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// reproducing kernel Hilbert space of k, and let \f$ \phi \f$</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// denote the corresponding feature map.</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// Then the SVM classifier is the function</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///     h(x) = \mathop{sign} (f(x))</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">///     f(x) = \langle w, \phi(x) \rangle + b</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// with coefficients w and b given by the (primal)</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// optimization problem</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">///     \min \frac{1}{2} \|w\|^2 + C \sum_i L(y_i, f(x_i)),</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// where \f$ L(y, f(x)) = \max\{0, 1 - y \cdot f(x)\} \f$</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">/// denotes the hinge loss.</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">///</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">/// For details refer to the paper:&lt;br/&gt;</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">/// &lt;p&gt;Support-Vector Networks. Corinna Cortes and Vladimir Vapnik,</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">/// Machine Learning, vol. 20 (1995), pp. 273-297.&lt;/p&gt;</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">/// or simply to the Wikipedia article:&lt;br/&gt;</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">/// http://en.wikipedia.org/wiki/Support_vector_machine</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> CacheType = <span class="keywordtype">float</span>&gt;</div>
<div class="foldopen" id="foldopen00074" data-start="{" data-end="};">
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html">   74</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_c_svm_trainer.html" title="Training of C-SVMs for binary classification.">CSvmTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer</a>&lt;</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    InputType, unsigned int, </div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    KernelClassifier&lt;InputType&gt;,</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    AbstractWeightedTrainer&lt;KernelClassifier&lt;InputType&gt; &gt; </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>&gt;</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>{</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer</a>&lt;</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_weighted_trainer.html#a11e51b154b87ed5e33b6ce2c830cd3d6">InputType</a>, <span class="keywordtype">unsigned</span> int, </div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a>,</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <a class="code hl_class" href="classshark_1_1_abstract_weighted_trainer.html" title="Superclass of weighted supervised learning algorithms.">AbstractWeightedTrainer&lt;KernelClassifier&lt;InputType&gt;</a> &gt; </div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>    &gt; <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment"></span> </div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">    /// \brief Convenience typedefs:</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">    /// this and many of the below typedefs build on the class template type CacheType.</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">    /// Simply changing that one template parameter CacheType thus allows to flexibly</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment">    /// switch between using float or double as type for caching the kernel values.</span></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment">    /// The default is float, offering sufficient accuracy in the vast majority</span></div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment">    /// of cases, at a memory cost of only four bytes. However, the template</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment">    /// parameter makes it easy to use double instead, (e.g., in case high</span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment">    /// accuracy training is needed).</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da">   96</a></span><span class="comment"></span>    <span class="keyword">typedef</span> CacheType <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>;</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span> </div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#abe46b77ba0322531583fe4fe08a5a0d5">   98</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#abe46b77ba0322531583fe4fe08a5a0d5">KernelType</a>;</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment"></span> </div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="comment">    //! Constructor</span></div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment">    //! \param  kernel         kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    //! \param  C              regularization parameter - always the &#39;true&#39; value of C, even when unconstrained is set</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment">    //! \param offset whether to train the svm with offset term</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">    //! \param  unconstrained  when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?</span></div>
<div class="foldopen" id="foldopen00105" data-start="{" data-end="}">
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#ab9e81925c8a8a25db71614edcae230f0">  105</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#ab9e81925c8a8a25db71614edcae230f0">CSvmTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, <span class="keywordtype">bool</span> offset, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>)</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>    : <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, offset, unconstrained), m_computeDerivative(false), m_McSvmType(<a class="code hl_enumeration" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">McSvm</a>::<a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a0a9b52fb6605edc74fd7d5359f34477e">WW</a>) <span class="comment">//make  Vapnik happy!</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>    { }</div>
</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    <span class="comment"></span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment">    //! Constructor</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    //! \param  kernel         kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    //! \param  negativeC   regularization parameter of the negative class (label 0)</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    //! \param  positiveC    regularization parameter of the positive class (label 1)</span></div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span><span class="comment">    //! \param offset whether to train the svm with offset term</span></div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">    //! \param  unconstrained  when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?</span></div>
<div class="foldopen" id="foldopen00115" data-start="{" data-end="}">
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#a055a354238f55019457ffcdc7c5d8d76">  115</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a055a354238f55019457ffcdc7c5d8d76">CSvmTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <span class="keywordtype">double</span> negativeC, <span class="keywordtype">double</span> positiveC, <span class="keywordtype">bool</span> offset, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>)</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    : <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>,negativeC, positiveC, offset, unconstrained), m_computeDerivative(false), m_McSvmType(<a class="code hl_enumeration" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">McSvm</a>::<a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a0a9b52fb6605edc74fd7d5359f34477e">WW</a>) <span class="comment">//make  Vapnik happy!</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    { }</div>
</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment"></span> </div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00120" data-start="{" data-end="}">
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#a9dc25f037d2af2005d1fbfd52ceffb55">  120</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a9dc25f037d2af2005d1fbfd52ceffb55" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;CSvmTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    </div>
<div class="foldopen" id="foldopen00123" data-start="{" data-end="}">
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#a7fdb7e988fa0949ca5e96faf9c7bcf48">  123</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a7fdb7e988fa0949ca5e96faf9c7bcf48">setComputeBinaryDerivative</a>(<span class="keywordtype">bool</span> compute){</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        m_computeDerivative = compute;</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>    }</div>
</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    <span class="comment"></span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment">    /// \brief sets the type of the multi-class svm used</span></div>
<div class="foldopen" id="foldopen00128" data-start="{" data-end="}">
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#ac9d92bc56fc0a8fa0d73631cb3cbf323">  128</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#ac9d92bc56fc0a8fa0d73631cb3cbf323" title="sets the type of the multi-class svm used">setMcSvmType</a>(<a class="code hl_enumeration" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">McSvm</a> type){</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        m_McSvmType = type;</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>    }</div>
</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span> </div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="comment"></span> </div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment">    /// \brief Train the C-SVM.</span></div>
<div class="foldopen" id="foldopen00134" data-start="{" data-end="}">
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#a9e801518bfba9d02e0749181a5deb0fc">  134</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a9e801518bfba9d02e0749181a5deb0fc" title="Train the C-SVM.">train</a>(<a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a>&amp; svm, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset)</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>    {</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        std::size_t classes = <a class="code hl_function" href="group__shark__globals.html#ga1fee3b5830ae11a78109e8c0265c6569" title="Return the number of classes of a set of class labels with unsigned int label encoding.">numberOfClasses</a>(dataset);</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        std::size_t ell = dataset.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordflow">if</span>(classes == 2){</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            <span class="comment">// prepare model</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>            </div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            <span class="keyword">auto</span>&amp; f = svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>();</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>            <span class="keywordflow">if</span> (f.basis() == dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>() &amp;&amp; f.kernel() == <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a> &amp;&amp; f.alpha().size1() == ell &amp;&amp; f.alpha().size2() == 1) {</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>                <span class="comment">// warm start, keep the alphas (possibly clipped)</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>                <span class="keywordflow">if</span> (this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>) f.offset() = RealVector(1);</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            }</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>                f.setStructure(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(), this-&gt;m_trainOffset);</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>            }</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>            </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>            <span class="comment">//dispatch to use the optimal implementation and solve the problem</span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            trainBinary(f,dataset);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            </div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">base_type::sparsify</a>())</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>                f.sparsify();</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>            <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        }</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        </div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        <span class="comment">//special case OVA </span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">if</span>(m_McSvmType == <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a586ba478b7ebec665f3df120799b6c2e">McSvm::OVA</a>){</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            trainOVA(svm,dataset);</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        }</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        </div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        <span class="comment">//general multiclass case: find correct dual formulation</span></div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        <span class="keywordtype">bool</span> sumToZero = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        <span class="keywordtype">bool</span> simplex = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <a class="code hl_class" href="classshark_1_1_qp_sparse_array.html" title="specialized container class for multi-class SVM problems">QpSparseArray&lt;QpFloatType&gt;</a> nu;</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        <a class="code hl_class" href="classshark_1_1_qp_sparse_array.html" title="specialized container class for multi-class SVM problems">QpSparseArray&lt;QpFloatType&gt;</a> M;</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        </div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <span class="keywordflow">switch</span> (m_McSvmType){</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a0a9b52fb6605edc74fd7d5359f34477e">McSvm::WW</a>:</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>                sumToZero = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>                simplex = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>                setupMcParametersWWCS(nu,M, classes);</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a8d7e99c73cd5a10adaaf4c9f9a520368">McSvm::CS</a>:</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>                sumToZero = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>                simplex=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>                setupMcParametersWWCS(nu,M, classes);</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a69120030cbf53ae8224b9b4865ab3945">McSvm::LLW</a>:</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>                sumToZero=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>                simplex = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>                setupMcParametersADMLLW(nu,M, classes);</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a292f535a3b6fe8853df2f03c8ed890a1">McSvm::ATM</a>:</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>                sumToZero=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>                simplex=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>                setupMcParametersATMATS(nu,M, classes);</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a600d2c32c47d0285e1df97492ed3bd35">McSvm::ATS</a>:</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>                sumToZero=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>                simplex = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>                setupMcParametersATMATS(nu,M, classes);</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a6fb4f22992a0d164b77267fde5477248">McSvm::ADM</a>:</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>                sumToZero=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>                simplex=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>                setupMcParametersADMLLW(nu,M, classes);</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a354624ba01e474ac153a71c5e0d3b266">McSvm::ReinforcedSvm</a>:</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>                sumToZero = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>                simplex = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>                setupMcParametersATMATS(nu,M, classes);</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112ada27331778aba3d85176f2c76f49bcc8">McSvm::MMR</a>:</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>                sumToZero = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>                simplex = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>                setupMcParametersMMR(nu,M, classes);</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a586ba478b7ebec665f3df120799b6c2e">McSvm::OVA</a>: <span class="comment">// handle OVA is switch statement to silence compiler warning</span></div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        }</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        </div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <span class="comment">//setup linear part</span></div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        RealMatrix linear(ell,M.<a class="code hl_function" href="classshark_1_1_qp_sparse_array.html#aeaeaa2435983010624e3d8149fb8cb26" title="number of columns">width</a>(),1.0);</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        <span class="keywordflow">if</span>(m_McSvmType == <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a354624ba01e474ac153a71c5e0d3b266">McSvm::ReinforcedSvm</a>){</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>            <span class="keyword">auto</span> <span class="keyword">const</span>&amp; labels = dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>();</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>            std::size_t i=0;</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>            <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y: labels.elements()){</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>                linear(i, y) = classes - 1.0;   <span class="comment">// self-margin target value of reinforced SVM loss</span></div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>                i++;</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>            }</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>        }</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>        </div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        <span class="comment">//solve dual</span></div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        RealMatrix alpha(ell,M.<a class="code hl_function" href="classshark_1_1_qp_sparse_array.html#aeaeaa2435983010624e3d8149fb8cb26" title="number of columns">width</a>(),0.0);</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>        RealVector bias(classes,0.0);</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>        <span class="keywordflow">if</span>(simplex)</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>            solveMcSimplex(sumToZero,nu,M,linear,alpha,bias,dataset);</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>            solveMcBox(sumToZero,nu,M,linear,alpha,bias,dataset);</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>        </div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>        </div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>        <span class="comment">// write the solution into the model</span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>        svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().setStructure(this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">m_kernel</a>,dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(),this-&gt;m_trainOffset,classes);</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>        </div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;ell; i++)</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>        {</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>            <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y = dataset.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(i).label;</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>            <span class="keywordflow">for</span> (std::size_t c=0; c&lt;classes; c++)</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>            {</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>                <span class="keywordtype">double</span> sum = 0.0;</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>                std::size_t r = alpha.size2() * y;</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>                <span class="keywordflow">for</span> (std::size_t p=0; p != alpha.size2(); p++, r++)</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>                    sum += nu(r, c) * alpha(i, p);</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>                svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().alpha(i,c) = sum;</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            }</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        }</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        </div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        <span class="keywordflow">if</span> (this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>) </div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>            svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().offset() = bias;</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span> </div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        <span class="keywordflow">if</span> (this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">sparsify</a>()) </div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>            svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().sparsify();</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>    }</div>
</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span><span class="comment"></span> </div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span><span class="comment">    /// \brief Train the C-SVM using weights.</span></div>
<div class="foldopen" id="foldopen00259" data-start="{" data-end="}">
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#a5444acc9ade8ab1c3550bd2efafa4e59">  259</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a5444acc9ade8ab1c3550bd2efafa4e59" title="Train the C-SVM using weights.">train</a>(<a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a>&amp; svm, <a class="code hl_class" href="classshark_1_1_weighted_labeled_data.html" title="Weighted data set for supervised learning.">WeightedLabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_function" href="group__shark__globals.html#ga1fee3b5830ae11a78109e8c0265c6569" title="Return the number of classes of a set of class labels with unsigned int label encoding.">numberOfClasses</a>(dataset) == 2, <span class="stringliteral">&quot;CSVM with weights is only implemented for binary problems&quot;</span>);</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        <span class="comment">// prepare model</span></div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        std::size_t n = dataset.numberOfElements();</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        <span class="keyword">auto</span>&amp; f = svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>();</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <span class="keywordflow">if</span> (f.basis() == dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ad11b0613785e1c6f36f6dd5d32662ead" title="Access to the inputs as a separate container.">inputs</a>() &amp;&amp; f.kernel() == <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a> &amp;&amp; f.alpha().size1() == n &amp;&amp; f.alpha().size2() == 1) {</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>            <span class="comment">// warm start, keep the alphas</span></div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>            <span class="keywordflow">if</span> (this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>) f.offset() = RealVector(1);</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>            <span class="keywordflow">else</span> f.offset() = RealVector();</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>        }</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>            f.setStructure(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ad11b0613785e1c6f36f6dd5d32662ead" title="Access to the inputs as a separate container.">inputs</a>(), this-&gt;m_trainOffset);</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>        }</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span> </div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        <span class="comment">//dispatch to use the optimal implementation and solve the problem</span></div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>        trainBinary(f, dataset);</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span> </div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">base_type::sparsify</a>()) f.sparsify();</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>    }</div>
</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>    </div>
<div class="foldopen" id="foldopen00279" data-start="{" data-end="}">
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno"><a class="line" href="classshark_1_1_c_svm_trainer.html#aa41c04a121457ef9ace253feac0bf42d">  279</a></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#aa41c04a121457ef9ace253feac0bf42d">get_db_dParams</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>        <span class="keywordflow">return</span> m_db_dParams;</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>    }</div>
</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span> </div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>    </div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>    <span class="keywordtype">void</span> solveMcSimplex(</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>        <span class="keywordtype">bool</span> sumToZero, <a class="code hl_class" href="classshark_1_1_qp_sparse_array.html" title="specialized container class for multi-class SVM problems">QpSparseArray&lt;QpFloatType&gt;</a> <span class="keyword">const</span>&amp; nu,<a class="code hl_class" href="classshark_1_1_qp_sparse_array.html" title="specialized container class for multi-class SVM problems">QpSparseArray&lt;QpFloatType&gt;</a> <span class="keyword">const</span>&amp; M, RealMatrix <span class="keyword">const</span>&amp; linear,</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>        RealMatrix&amp; alpha, RealVector&amp; bias, </div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>        <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>    ){</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_kernel_matrix.html" title="Kernel Gram matrix.">KernelMatrix&lt;InputType, QpFloatType&gt;</a> KernelMatrixType;</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_cached_matrix.html" title="Efficient quadratic matrix cache.">CachedMatrix&lt; KernelMatrixType &gt;</a> CachedMatrixType;</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_precomputed_matrix.html" title="Precomputed version of a matrix for quadratic programming.">PrecomputedMatrix&lt; KernelMatrixType &gt;</a> PrecomputedMatrixType;</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>        </div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>        KernelMatrixType km(*<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>());</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>        <span class="comment">// solve the problem</span></div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">base_type::precomputeKernel</a>())</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>        {</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>            PrecomputedMatrixType matrix(&amp;km);</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>            <a class="code hl_class" href="classshark_1_1_qp_mc_simplex_decomp.html">QpMcSimplexDecomp&lt; PrecomputedMatrixType&gt;</a> problem(matrix, M, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), linear, this-&gt;C());</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>            <a class="code hl_struct" href="structshark_1_1_qp_solution_properties.html" title="properties of the solution of a quadratic program">QpSolutionProperties</a>&amp; prop = <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>;</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>            problem.setShrinking(<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>            <span class="keywordflow">if</span>(this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>){</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>                <a class="code hl_class" href="classshark_1_1_bias_solver_simplex.html">BiasSolverSimplex&lt;PrecomputedMatrixType&gt;</a> biasSolver(&amp;problem);</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>                biasSolver.solve(bias,<a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>,nu,sumToZero, &amp;prop);</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>            }</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span>            <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>                QpSolver&lt;QpMcSimplexDecomp&lt; PrecomputedMatrixType&gt; &gt; solver(problem);</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>                solver.solve( <a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>, &amp;prop);</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>            }</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>            alpha = problem.solution();</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>        }</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>        {</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>            CachedMatrixType matrix(&amp;km, <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#a0382adafdbe762f4456dc7858ea120c2" title="Number of values in the kernel cache. The size of the cache in bytes is the size of one entry (4 for ...">base_type::m_cacheSize</a>);</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>            QpMcSimplexDecomp&lt; CachedMatrixType&gt; problem(matrix, M, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), linear, this-&gt;C());</div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>            QpSolutionProperties&amp; prop = <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>;</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>            problem.setShrinking(<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>            <span class="keywordflow">if</span>(this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>){</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>                BiasSolverSimplex&lt;CachedMatrixType&gt; biasSolver(&amp;problem);</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>                biasSolver.solve(bias,<a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>,nu,sumToZero, &amp;prop);</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>            }</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>            <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>                QpSolver&lt;QpMcSimplexDecomp&lt; CachedMatrixType&gt; &gt; solver(problem);</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>                solver.solve( <a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>, &amp;prop);</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>            }</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>            alpha = problem.solution();</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>        }</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> = km.getAccessCount();</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>    }</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>    </div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>    <span class="keywordtype">void</span> solveMcBox(</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>        <span class="keywordtype">bool</span> sumToZero, QpSparseArray&lt;QpFloatType&gt; <span class="keyword">const</span>&amp; nu,QpSparseArray&lt;QpFloatType&gt; <span class="keyword">const</span>&amp; M, RealMatrix <span class="keyword">const</span>&amp; linear,</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>        RealMatrix&amp; alpha, RealVector&amp; bias, </div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>        LabeledData&lt;InputType, unsigned int&gt; <span class="keyword">const</span>&amp; dataset</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>    ){</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>        <span class="keyword">typedef</span> KernelMatrix&lt;InputType, QpFloatType&gt; KernelMatrixType;</div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span>        <span class="keyword">typedef</span> CachedMatrix&lt; KernelMatrixType &gt; CachedMatrixType;</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span>        <span class="keyword">typedef</span> PrecomputedMatrix&lt; KernelMatrixType &gt; PrecomputedMatrixType;</div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>        </div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>        KernelMatrixType km(*<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.inputs());</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>        <span class="comment">// solve the problem</span></div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">base_type::precomputeKernel</a>())</div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span>        {</div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span>            PrecomputedMatrixType matrix(&amp;km);</div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>            QpMcBoxDecomp&lt; PrecomputedMatrixType&gt; problem(matrix, M, dataset.labels(), linear, this-&gt;C());</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>            QpSolutionProperties&amp; prop = <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>;</div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span>            problem.setShrinking(<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>            <span class="keywordflow">if</span>(this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>){</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>                BiasSolver&lt;PrecomputedMatrixType&gt; biasSolver(&amp;problem);</div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>                biasSolver.solve(bias,<a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>,nu, sumToZero, &amp;prop);</div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span>            }</div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>            <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>                QpSolver&lt;QpMcBoxDecomp&lt; PrecomputedMatrixType&gt; &gt; solver(problem);</div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span>                solver.solve( <a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>, &amp;prop);</div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span>            }</div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>            alpha = problem.solution();</div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span>        }</div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span>        {</div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>            CachedMatrixType matrix(&amp;km, <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#a0382adafdbe762f4456dc7858ea120c2" title="Number of values in the kernel cache. The size of the cache in bytes is the size of one entry (4 for ...">base_type::m_cacheSize</a>);</div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>            QpMcBoxDecomp&lt; CachedMatrixType&gt; problem(matrix, M, dataset.labels(), linear, this-&gt;C());</div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span>            QpSolutionProperties&amp; prop = <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>;</div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>            problem.setShrinking(<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>            <span class="keywordflow">if</span>(this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>){</div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span>                BiasSolver&lt;CachedMatrixType&gt; biasSolver(&amp;problem);</div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span>                biasSolver.solve(bias,<a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>,nu, sumToZero, &amp;prop);</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span>            }</div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>            <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span>                QpSolver&lt;QpMcBoxDecomp&lt; CachedMatrixType&gt; &gt; solver(problem);</div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span>                solver.solve( <a class="code hl_variable" href="classshark_1_1_qp_config.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>, &amp;prop);</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>            }</div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span>            alpha = problem.solution();</div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span>        }</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> = km.getAccessCount();</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>    }</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>    </div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Trainer&gt;</div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>    <span class="keywordtype">void</span> trainMc(KernelClassifier&lt;InputType&gt;&amp; svm, LabeledData&lt;InputType, unsigned int&gt; <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span>        Trainer trainer(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>,this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>(),this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>);</div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>        trainer.stoppingCondition() = this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">stoppingCondition</a>();</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span>        trainer.precomputeKernel() = this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">precomputeKernel</a>();</div>
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span>        trainer.sparsify() = this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">sparsify</a>();</div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>        trainer.shrinking() = this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#ab538a92231c05e20575f181b06c5689d" title="Flag for shrinking in the decomposition solver.">shrinking</a>();</div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span>        trainer.s2do() = this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a5a4d6d3ff5c8acbd809108786e973f7a" title="Flag for S2DO (instead of SMO)">s2do</a>();</div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span>        trainer.verbosity() = this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a71328214090e442c9fee46103868b0ca" title="Verbosity level of the solver.">verbosity</a>();</div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>        trainer.setCacheSize(this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a954cc587b52b4ec5a347134804dfc812">cacheSize</a>());</div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span>        trainer.train(svm,dataset);</div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span>        this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">solutionProperties</a>() = trainer.solutionProperties();</div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> = trainer.accessCount();</div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span>    }</div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>    </div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span>    <span class="keywordtype">void</span> setupMcParametersWWCS(QpSparseArray&lt;QpFloatType&gt;&amp; nu,QpSparseArray&lt;QpFloatType&gt;&amp; M, std::size_t classes)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno">  393</span>        nu.resize(classes * (classes-1), classes, 2*classes*(classes-1));</div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r=0, y=0; y&lt;classes; y++)</div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span>        {</div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span>            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> p=0, pp=0; p&lt;classes-1; p++, pp++, r++)</div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span>            {</div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span>                <span class="keywordflow">if</span> (pp == y) pp++;</div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno">  399</span>                <span class="keywordflow">if</span> (y &lt; pp)</div>
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno">  400</span>                {</div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno">  401</span>                    nu.add(r, y, 0.5);</div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno">  402</span>                    nu.add(r, pp, -0.5);</div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno">  403</span>                }</div>
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno">  404</span>                <span class="keywordflow">else</span></div>
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno">  405</span>                {</div>
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno">  406</span>                    nu.add(r, pp, -0.5);</div>
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno">  407</span>                    nu.add(r, y, 0.5);</div>
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno">  408</span>                }</div>
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno">  409</span>            }</div>
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno">  410</span>        }</div>
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno">  411</span>        </div>
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno">  412</span>        M.resize(classes * (classes-1) * classes, classes-1, 2 * classes * (classes-1) * (classes-1));</div>
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno">  413</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r=0, yv=0; yv&lt;classes; yv++)</div>
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno">  414</span>        {</div>
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno">  415</span>            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pv=0, ppv=0; pv&lt;classes-1; pv++, ppv++)</div>
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno">  416</span>            {</div>
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno">  417</span>                <span class="keywordflow">if</span> (ppv == yv) ppv++;</div>
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno">  418</span>                <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yw=0; yw&lt;classes; yw++, r++)</div>
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno">  419</span>                {</div>
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno">  420</span>                    <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> baseM = (yv == yw ? (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25 : (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.0) - (ppv == yw ? (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25 : (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.0);</div>
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno">  421</span>                    M.setDefaultValue(r, baseM);</div>
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno">  422</span>                    <span class="keywordflow">if</span> (yv == yw)</div>
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno">  423</span>                    {</div>
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno">  424</span>                        M.add(r, ppv - (ppv &gt;= yw ? 1 : 0), baseM + (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25);</div>
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno">  425</span>                    }</div>
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno">  426</span>                    <span class="keywordflow">else</span> <span class="keywordflow">if</span> (ppv == yw)</div>
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno">  427</span>                    {</div>
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno">  428</span>                        M.add(r, yv - (yv &gt;= yw ? 1 : 0), baseM - (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25);</div>
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno">  429</span>                    }</div>
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno">  430</span>                    <span class="keywordflow">else</span></div>
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno">  431</span>                    {</div>
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno">  432</span>                        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pw = ppv - (ppv &gt;= yw ? 1 : 0);</div>
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno">  433</span>                        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pw2 = yv - (yv &gt;= yw ? 1 : 0);</div>
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno">  434</span>                        <span class="keywordflow">if</span> (pw &lt; pw2)</div>
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno">  435</span>                        {</div>
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno">  436</span>                            M.add(r, pw, baseM + (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25);</div>
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno">  437</span>                            M.add(r, pw2, baseM - (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25);</div>
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno">  438</span>                        }</div>
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno">  439</span>                        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno">  440</span>                        {</div>
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno">  441</span>                            M.add(r, pw2, baseM - (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25);</div>
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno">  442</span>                            M.add(r, pw, baseM + (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)0.25);</div>
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno">  443</span>                        }</div>
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno">  444</span>                    }</div>
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno">  445</span>                }</div>
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno">  446</span>            }</div>
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno">  447</span>        }</div>
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno">  448</span>    }</div>
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno">  449</span>    </div>
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno">  450</span>    <span class="keywordtype">void</span> setupMcParametersATMATS(QpSparseArray&lt;QpFloatType&gt;&amp; nu,QpSparseArray&lt;QpFloatType&gt;&amp; M, std::size_t classes)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno">  451</span>        nu.resize(classes*classes, classes, classes*classes);</div>
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno">  452</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r=0, y=0; y&lt;classes; y++)</div>
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno">  453</span>        {</div>
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno">  454</span>            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> p=0; p&lt;classes; p++, r++)</div>
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno">  455</span>            {</div>
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno">  456</span>                nu.add(r, p, (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)((p == y) ? 1.0 : -1.0));</div>
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno">  457</span>            }</div>
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno">  458</span>        }</div>
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno">  459</span>        </div>
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno">  460</span>        M.resize(classes * classes * classes, classes, 2 * classes * classes * classes);</div>
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno">  461</span>        <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> c_ne = (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)(-1.0 / (<span class="keywordtype">double</span>)classes);</div>
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno">  462</span>        <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> c_eq = (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)1.0 + c_ne;</div>
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno">  463</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r=0, yv=0; yv&lt;classes; yv++)</div>
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno">  464</span>        {</div>
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno">  465</span>            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pv=0; pv&lt;classes; pv++)</div>
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno">  466</span>            {</div>
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno">  467</span>                <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> sign = <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>((yv == pv) ? -1 : 1);<span class="comment">//cast to keep MSVC happy...</span></div>
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno">  468</span>                <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yw=0; yw&lt;classes; yw++, r++)</div>
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno">  469</span>                {</div>
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno">  470</span>                    M.setDefaultValue(r, sign * c_ne);</div>
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno">  471</span>                    <span class="keywordflow">if</span> (yw == pv)</div>
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno">  472</span>                    {</div>
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno">  473</span>                        M.add(r, pv, -sign * c_eq);</div>
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno">  474</span>                    }</div>
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno">  475</span>                    <span class="keywordflow">else</span></div>
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno">  476</span>                    {</div>
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno">  477</span>                        M.add(r, pv, sign * c_eq);</div>
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno">  478</span>                        M.add(r, yw, -sign * c_ne);</div>
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno">  479</span>                    }</div>
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno">  480</span>                }</div>
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno">  481</span>            }</div>
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno">  482</span>        }</div>
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno">  483</span>    }</div>
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno">  484</span>    </div>
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno">  485</span>    <span class="keywordtype">void</span> setupMcParametersADMLLW(QpSparseArray&lt;QpFloatType&gt;&amp; nu,QpSparseArray&lt;QpFloatType&gt;&amp; M, std::size_t classes)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno">  486</span>        nu.resize(classes * (classes-1), classes, classes*(classes-1));</div>
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno">  487</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r=0, y=0; y&lt;classes; y++)</div>
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno">  488</span>        {</div>
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno">  489</span>            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> p=0, pp=0; p&lt;classes-1; p++, pp++, r++)</div>
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno">  490</span>            {</div>
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno">  491</span>                <span class="keywordflow">if</span> (pp == y) pp++;</div>
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno">  492</span>                nu.add(r, pp, (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)-1.0);</div>
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno">  493</span>            }</div>
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno">  494</span>        }</div>
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno">  495</span>        </div>
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno">  496</span>        M.resize(classes * (classes-1) * classes, classes-1, classes * (classes-1) * (classes-1));</div>
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno">  497</span>        <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> mood = (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)(-1.0 / (<span class="keywordtype">double</span>)classes);</div>
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno">  498</span>        <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> val = (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)1.0 + mood;</div>
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno">  499</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r=0, yv=0; yv&lt;classes; yv++)</div>
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno">  500</span>        {</div>
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno">  501</span>            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pv=0, ppv=0; pv&lt;classes-1; pv++, ppv++)</div>
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno">  502</span>            {</div>
<div class="line"><a id="l00503" name="l00503"></a><span class="lineno">  503</span>                <span class="keywordflow">if</span> (ppv == yv) ppv++;</div>
<div class="line"><a id="l00504" name="l00504"></a><span class="lineno">  504</span>                <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yw=0; yw&lt;classes; yw++, r++)</div>
<div class="line"><a id="l00505" name="l00505"></a><span class="lineno">  505</span>                {</div>
<div class="line"><a id="l00506" name="l00506"></a><span class="lineno">  506</span>                    M.setDefaultValue(r, mood);</div>
<div class="line"><a id="l00507" name="l00507"></a><span class="lineno">  507</span>                    <span class="keywordflow">if</span> (ppv != yw)</div>
<div class="line"><a id="l00508" name="l00508"></a><span class="lineno">  508</span>                    {</div>
<div class="line"><a id="l00509" name="l00509"></a><span class="lineno">  509</span>                        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> pw = ppv - (ppv &gt; yw ? 1 : 0);</div>
<div class="line"><a id="l00510" name="l00510"></a><span class="lineno">  510</span>                        M.add(r, pw, val);</div>
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno">  511</span>                    }</div>
<div class="line"><a id="l00512" name="l00512"></a><span class="lineno">  512</span>                }</div>
<div class="line"><a id="l00513" name="l00513"></a><span class="lineno">  513</span>            }</div>
<div class="line"><a id="l00514" name="l00514"></a><span class="lineno">  514</span>        }</div>
<div class="line"><a id="l00515" name="l00515"></a><span class="lineno">  515</span>    }</div>
<div class="line"><a id="l00516" name="l00516"></a><span class="lineno">  516</span>    </div>
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno">  517</span>    <span class="keywordtype">void</span> setupMcParametersMMR(QpSparseArray&lt;QpFloatType&gt;&amp; nu,QpSparseArray&lt;QpFloatType&gt;&amp; M, std::size_t classes)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno">  518</span>        nu.resize(classes, classes, classes);</div>
<div class="line"><a id="l00519" name="l00519"></a><span class="lineno">  519</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> y=0; y&lt;classes; y++) </div>
<div class="line"><a id="l00520" name="l00520"></a><span class="lineno">  520</span>            nu.add(y, y, 1.0);</div>
<div class="line"><a id="l00521" name="l00521"></a><span class="lineno">  521</span> </div>
<div class="line"><a id="l00522" name="l00522"></a><span class="lineno">  522</span>        M.resize(classes * classes, 1, classes);</div>
<div class="line"><a id="l00523" name="l00523"></a><span class="lineno">  523</span>        <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> mood = (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)(-1.0 / (<span class="keywordtype">double</span>)classes);</div>
<div class="line"><a id="l00524" name="l00524"></a><span class="lineno">  524</span>        <a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a> val = (<a class="code hl_typedef" href="classshark_1_1_c_svm_trainer.html#a992b58169d0ffd5be0d355b6432f32da" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>)1.0 + mood;</div>
<div class="line"><a id="l00525" name="l00525"></a><span class="lineno">  525</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> r=0, yv=0; yv&lt;classes; yv++)</div>
<div class="line"><a id="l00526" name="l00526"></a><span class="lineno">  526</span>        {</div>
<div class="line"><a id="l00527" name="l00527"></a><span class="lineno">  527</span>            <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> yw=0; yw&lt;classes; yw++, r++)</div>
<div class="line"><a id="l00528" name="l00528"></a><span class="lineno">  528</span>            {</div>
<div class="line"><a id="l00529" name="l00529"></a><span class="lineno">  529</span>                M.setDefaultValue(r, mood);</div>
<div class="line"><a id="l00530" name="l00530"></a><span class="lineno">  530</span>                <span class="keywordflow">if</span> (yv == yw) M.add(r, 0, val);</div>
<div class="line"><a id="l00531" name="l00531"></a><span class="lineno">  531</span>            }</div>
<div class="line"><a id="l00532" name="l00532"></a><span class="lineno">  532</span>        }</div>
<div class="line"><a id="l00533" name="l00533"></a><span class="lineno">  533</span>    }</div>
<div class="line"><a id="l00534" name="l00534"></a><span class="lineno">  534</span>    </div>
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno">  535</span>    <span class="keywordtype">void</span> trainOVA(KernelClassifier&lt;InputType&gt;&amp; svm, <span class="keyword">const</span> LabeledData&lt;InputType, unsigned int&gt;&amp; dataset){</div>
<div class="line"><a id="l00536" name="l00536"></a><span class="lineno">  536</span>        std::size_t classes = <a class="code hl_function" href="group__shark__globals.html#ga1fee3b5830ae11a78109e8c0265c6569" title="Return the number of classes of a set of class labels with unsigned int label encoding.">numberOfClasses</a>(dataset);</div>
<div class="line"><a id="l00537" name="l00537"></a><span class="lineno">  537</span>        svm.decisionFunction().setStructure(this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">m_kernel</a>,dataset.inputs(),this-&gt;m_trainOffset,classes);</div>
<div class="line"><a id="l00538" name="l00538"></a><span class="lineno">  538</span>        </div>
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno">  539</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.type = <a class="code hl_enumvalue" href="namespaceshark.html#a2d5e9a415ae7e8dd41caf883e1873540ab544fb3e76bdbaf78448f2416367ccc7">QpNone</a>;</div>
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno">  540</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a754cf16ef14ec337a626ab31c11ac444" title="typically the maximal KKT violation">accuracy</a> = 0.0;</div>
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno">  541</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#aa1b7fb15931dfebb70364b0bf949fa15" title="number of decomposition iterations">iterations</a> = 0;</div>
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno">  542</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#aa3b4e5dcba8e39ee858c6ea36c8879b3" title="value of the objective function">value</a> = 0.0;</div>
<div class="line"><a id="l00543" name="l00543"></a><span class="lineno">  543</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a965ff3df7a7c9ae101b0b06e82921c91" title="training time">seconds</a> = 0.0;</div>
<div class="line"><a id="l00544" name="l00544"></a><span class="lineno">  544</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> c=0; c&lt;classes; c++)</div>
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno">  545</span>        {</div>
<div class="line"><a id="l00546" name="l00546"></a><span class="lineno">  546</span>            LabeledData&lt;InputType, unsigned int&gt; bindata = <a class="code hl_function" href="group__shark__globals.html#gac1a150d7458195ce9212917b4956a4b7" title="Construct a binary (two-class) one-versus-rest problem from a multi-class problem.">oneVersusRestProblem</a>(dataset, c);</div>
<div class="line"><a id="l00547" name="l00547"></a><span class="lineno">  547</span>            KernelClassifier&lt;InputType&gt; binsvm;</div>
<div class="line"><a id="l00548" name="l00548"></a><span class="lineno">  548</span><span class="comment">// TODO: maybe build the Quadratic programs directly,</span></div>
<div class="line"><a id="l00549" name="l00549"></a><span class="lineno">  549</span><span class="comment">//       in order to profit from cached and</span></div>
<div class="line"><a id="l00550" name="l00550"></a><span class="lineno">  550</span><span class="comment">//       in particular from precomputed kernel</span></div>
<div class="line"><a id="l00551" name="l00551"></a><span class="lineno">  551</span><span class="comment">//       entries!</span></div>
<div class="line"><a id="l00552" name="l00552"></a><span class="lineno">  552</span>            CSvmTrainer&lt;InputType, QpFloatType&gt; bintrainer(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>(),this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>);</div>
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno">  553</span>            bintrainer.setCacheSize(this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a954cc587b52b4ec5a347134804dfc812">cacheSize</a>());</div>
<div class="line"><a id="l00554" name="l00554"></a><span class="lineno">  554</span>            bintrainer.sparsify() = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00555" name="l00555"></a><span class="lineno">  555</span>            bintrainer.stoppingCondition() = <a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">base_type::stoppingCondition</a>();</div>
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno">  556</span>            bintrainer.precomputeKernel() = <a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">base_type::precomputeKernel</a>();      <span class="comment">// sub-optimal!</span></div>
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno">  557</span>            bintrainer.shrinking() = <a class="code hl_function" href="classshark_1_1_qp_config.html#ab538a92231c05e20575f181b06c5689d" title="Flag for shrinking in the decomposition solver.">base_type::shrinking</a>();</div>
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno">  558</span>            bintrainer.s2do() = <a class="code hl_function" href="classshark_1_1_qp_config.html#a5a4d6d3ff5c8acbd809108786e973f7a" title="Flag for S2DO (instead of SMO)">base_type::s2do</a>();</div>
<div class="line"><a id="l00559" name="l00559"></a><span class="lineno">  559</span>            bintrainer.verbosity() = <a class="code hl_function" href="classshark_1_1_qp_config.html#a71328214090e442c9fee46103868b0ca" title="Verbosity level of the solver.">base_type::verbosity</a>();</div>
<div class="line"><a id="l00560" name="l00560"></a><span class="lineno">  560</span>            bintrainer.train(binsvm, bindata);</div>
<div class="line"><a id="l00561" name="l00561"></a><span class="lineno">  561</span>            <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#aa1b7fb15931dfebb70364b0bf949fa15" title="number of decomposition iterations">iterations</a> += bintrainer.solutionProperties().iterations;</div>
<div class="line"><a id="l00562" name="l00562"></a><span class="lineno">  562</span>            <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a965ff3df7a7c9ae101b0b06e82921c91" title="training time">seconds</a> += bintrainer.solutionProperties().seconds;</div>
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno">  563</span>            <a class="code hl_variable" href="classshark_1_1_qp_config.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a754cf16ef14ec337a626ab31c11ac444" title="typically the maximal KKT violation">accuracy</a> = std::max(<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>().accuracy, bintrainer.solutionProperties().<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a754cf16ef14ec337a626ab31c11ac444" title="typically the maximal KKT violation">accuracy</a>);</div>
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno">  564</span>            column(svm.decisionFunction().alpha(), c) = column(binsvm.decisionFunction().alpha(), 0);</div>
<div class="line"><a id="l00565" name="l00565"></a><span class="lineno">  565</span>            <span class="keywordflow">if</span> (this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>)</div>
<div class="line"><a id="l00566" name="l00566"></a><span class="lineno">  566</span>                svm.decisionFunction().offset(c) = binsvm.decisionFunction().offset(0);</div>
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno">  567</span>            <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> += bintrainer.accessCount();</div>
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno">  568</span>        }</div>
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno">  569</span> </div>
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno">  570</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">base_type::sparsify</a>()) </div>
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno">  571</span>            svm.decisionFunction().sparsify();</div>
<div class="line"><a id="l00572" name="l00572"></a><span class="lineno">  572</span>    }</div>
<div class="line"><a id="l00573" name="l00573"></a><span class="lineno">  573</span>    </div>
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno">  574</span>    <span class="comment">//by default the normal unoptimized kernel matrix is used</span></div>
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno">  575</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> T, <span class="keyword">class</span> DatasetTypeT&gt;</div>
<div class="line"><a id="l00576" name="l00576"></a><span class="lineno">  576</span>    <span class="keywordtype">void</span> trainBinary(KernelExpansion&lt;T&gt;&amp; svm, DatasetTypeT <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00577" name="l00577"></a><span class="lineno">  577</span>        KernelMatrix&lt;T, QpFloatType&gt; km(*<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.inputs());</div>
<div class="line"><a id="l00578" name="l00578"></a><span class="lineno">  578</span>        trainBinary(km,svm,dataset);</div>
<div class="line"><a id="l00579" name="l00579"></a><span class="lineno">  579</span>    }</div>
<div class="line"><a id="l00580" name="l00580"></a><span class="lineno">  580</span>    </div>
<div class="line"><a id="l00581" name="l00581"></a><span class="lineno">  581</span>    <span class="comment">//in the case of a gaussian kernel and sparse vectors, we can use an optimized approach</span></div>
<div class="line"><a id="l00582" name="l00582"></a><span class="lineno">  582</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> T, <span class="keyword">class</span> DatasetTypeT&gt;</div>
<div class="line"><a id="l00583" name="l00583"></a><span class="lineno">  583</span>    <span class="keywordtype">void</span> trainBinary(KernelExpansion&lt;CompressedRealVector&gt;&amp; svm, DatasetTypeT <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00584" name="l00584"></a><span class="lineno">  584</span>        <span class="comment">//check whether a gaussian kernel is used</span></div>
<div class="line"><a id="l00585" name="l00585"></a><span class="lineno">  585</span>        <span class="keyword">typedef</span> GaussianRbfKernel&lt;CompressedRealVector&gt; Gaussian;</div>
<div class="line"><a id="l00586" name="l00586"></a><span class="lineno">  586</span>        Gaussian <span class="keyword">const</span>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a> = <span class="keyword">dynamic_cast&lt;</span>Gaussian const*<span class="keyword">&gt;</span> (<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>);</div>
<div class="line"><a id="l00587" name="l00587"></a><span class="lineno">  587</span>        <span class="keywordflow">if</span>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a> != 0){<span class="comment">//jep, use optimized kernel matrix</span></div>
<div class="line"><a id="l00588" name="l00588"></a><span class="lineno">  588</span>            GaussianKernelMatrix&lt;CompressedRealVector,QpFloatType&gt; km(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>-&gt;gamma(),dataset.inputs());</div>
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno">  589</span>            trainBinary(km,svm,dataset);</div>
<div class="line"><a id="l00590" name="l00590"></a><span class="lineno">  590</span>        }</div>
<div class="line"><a id="l00591" name="l00591"></a><span class="lineno">  591</span>        <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00592" name="l00592"></a><span class="lineno">  592</span>            KernelMatrix&lt;CompressedRealVector, QpFloatType&gt; km(*<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.inputs());</div>
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno">  593</span>            trainBinary(km,svm,dataset);</div>
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno">  594</span>        }</div>
<div class="line"><a id="l00595" name="l00595"></a><span class="lineno">  595</span>    }</div>
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno">  596</span>    </div>
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno">  597</span>    <span class="comment">//create the problem for the unweighted datasets</span></div>
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno">  598</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Matrix, <span class="keyword">class</span> T&gt;</div>
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno">  599</span>    <span class="keywordtype">void</span> trainBinary(Matrix&amp; km, KernelExpansion&lt;T&gt;&amp; svm, LabeledData&lt;T, unsigned int&gt; <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno">  600</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">QpConfig::precomputeKernel</a>())</div>
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno">  601</span>        {</div>
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno">  602</span>            PrecomputedMatrix&lt;Matrix&gt; matrix(&amp;km);</div>
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno">  603</span>            CSVMProblem&lt;PrecomputedMatrix&lt;Matrix&gt; &gt; svmProblem(matrix,dataset.labels(),<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a>);</div>
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno">  604</span>            optimize(svm,svmProblem,dataset);</div>
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno">  605</span>        }</div>
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno">  606</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno">  607</span>        {</div>
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno">  608</span>            CachedMatrix&lt;Matrix&gt; matrix(&amp;km);</div>
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno">  609</span>            CSVMProblem&lt;CachedMatrix&lt;Matrix&gt; &gt; svmProblem(matrix,dataset.labels(),<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a>);</div>
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno">  610</span>            optimize(svm,svmProblem,dataset);</div>
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno">  611</span>        }</div>
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno">  612</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> = km.getAccessCount();</div>
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno">  613</span>    }</div>
<div class="line"><a id="l00614" name="l00614"></a><span class="lineno">  614</span>    </div>
<div class="line"><a id="l00615" name="l00615"></a><span class="lineno">  615</span>    <span class="comment">// create the problem for the weighted datasets</span></div>
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno">  616</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Matrix, <span class="keyword">class</span> T&gt;</div>
<div class="line"><a id="l00617" name="l00617"></a><span class="lineno">  617</span>    <span class="keywordtype">void</span> trainBinary(Matrix&amp; km, KernelExpansion&lt;T&gt;&amp; svm, WeightedLabeledData&lt;T, unsigned int&gt; <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00618" name="l00618"></a><span class="lineno">  618</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">QpConfig::precomputeKernel</a>())</div>
<div class="line"><a id="l00619" name="l00619"></a><span class="lineno">  619</span>        {</div>
<div class="line"><a id="l00620" name="l00620"></a><span class="lineno">  620</span>            PrecomputedMatrix&lt;Matrix&gt; matrix(&amp;km);</div>
<div class="line"><a id="l00621" name="l00621"></a><span class="lineno">  621</span>            GeneralQuadraticProblem&lt;PrecomputedMatrix&lt;Matrix&gt; &gt; svmProblem(</div>
<div class="line"><a id="l00622" name="l00622"></a><span class="lineno">  622</span>                matrix,dataset.labels(),dataset.weights(),<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a></div>
<div class="line"><a id="l00623" name="l00623"></a><span class="lineno">  623</span>            );</div>
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno">  624</span>            optimize(svm,svmProblem,dataset.data());</div>
<div class="line"><a id="l00625" name="l00625"></a><span class="lineno">  625</span>        }</div>
<div class="line"><a id="l00626" name="l00626"></a><span class="lineno">  626</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00627" name="l00627"></a><span class="lineno">  627</span>        {</div>
<div class="line"><a id="l00628" name="l00628"></a><span class="lineno">  628</span>            CachedMatrix&lt;Matrix&gt; matrix(&amp;km);</div>
<div class="line"><a id="l00629" name="l00629"></a><span class="lineno">  629</span>            GeneralQuadraticProblem&lt;CachedMatrix&lt;Matrix&gt; &gt; svmProblem(</div>
<div class="line"><a id="l00630" name="l00630"></a><span class="lineno">  630</span>                matrix,dataset.labels(),dataset.weights(),<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a></div>
<div class="line"><a id="l00631" name="l00631"></a><span class="lineno">  631</span>            );</div>
<div class="line"><a id="l00632" name="l00632"></a><span class="lineno">  632</span>            optimize(svm,svmProblem,dataset.data());</div>
<div class="line"><a id="l00633" name="l00633"></a><span class="lineno">  633</span>        }</div>
<div class="line"><a id="l00634" name="l00634"></a><span class="lineno">  634</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> = km.getAccessCount();</div>
<div class="line"><a id="l00635" name="l00635"></a><span class="lineno">  635</span>    }</div>
<div class="line"><a id="l00636" name="l00636"></a><span class="lineno">  636</span>    </div>
<div class="line"><a id="l00637" name="l00637"></a><span class="lineno">  637</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> SVMProblemType&gt;</div>
<div class="line"><a id="l00638" name="l00638"></a><span class="lineno">  638</span>    <span class="keywordtype">void</span> optimize(KernelExpansion&lt;InputType&gt;&amp; svm, SVMProblemType&amp; svmProblem, LabeledData&lt;InputType, unsigned int&gt; <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00639" name="l00639"></a><span class="lineno">  639</span>        <span class="keywordflow">if</span> (this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>)</div>
<div class="line"><a id="l00640" name="l00640"></a><span class="lineno">  640</span>        {</div>
<div class="line"><a id="l00641" name="l00641"></a><span class="lineno">  641</span>            <span class="keyword">typedef</span> SvmShrinkingProblem&lt;SVMProblemType&gt; ProblemType;</div>
<div class="line"><a id="l00642" name="l00642"></a><span class="lineno">  642</span>            ProblemType problem(svmProblem,<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00643" name="l00643"></a><span class="lineno">  643</span>            QpSolver&lt; ProblemType &gt; solver(problem);</div>
<div class="line"><a id="l00644" name="l00644"></a><span class="lineno">  644</span>            <span class="comment">// truncate the existing solution to the bounds</span></div>
<div class="line"><a id="l00645" name="l00645"></a><span class="lineno">  645</span>            RealVector <span class="keyword">const</span>&amp; reg = this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#ad06550eb45e46ff02e4789ea1b916c75">regularizationParameters</a>();</div>
<div class="line"><a id="l00646" name="l00646"></a><span class="lineno">  646</span>            <span class="keywordtype">double</span> C_minus = reg(0);</div>
<div class="line"><a id="l00647" name="l00647"></a><span class="lineno">  647</span>            <span class="keywordtype">double</span> C_plus = (reg.size() == 1) ? reg(0) : reg(1);</div>
<div class="line"><a id="l00648" name="l00648"></a><span class="lineno">  648</span>            std::size_t i=0;</div>
<div class="line"><a id="l00649" name="l00649"></a><span class="lineno">  649</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> label : dataset.labels().elements()) {</div>
<div class="line"><a id="l00650" name="l00650"></a><span class="lineno">  650</span>                <span class="keywordtype">double</span> a = svm.alpha()(i, 0);</div>
<div class="line"><a id="l00651" name="l00651"></a><span class="lineno">  651</span>                <span class="keywordflow">if</span> (label == 0) a = std::max(std::min(a, 0.0), -C_minus);</div>
<div class="line"><a id="l00652" name="l00652"></a><span class="lineno">  652</span>                <span class="keywordflow">else</span>            a = std::min(std::max(a, 0.0), C_plus);</div>
<div class="line"><a id="l00653" name="l00653"></a><span class="lineno">  653</span>                svm.alpha()(i, 0) = a;</div>
<div class="line"><a id="l00654" name="l00654"></a><span class="lineno">  654</span>                i++;</div>
<div class="line"><a id="l00655" name="l00655"></a><span class="lineno">  655</span>            }</div>
<div class="line"><a id="l00656" name="l00656"></a><span class="lineno">  656</span>            problem.setInitialSolution(blas::column(svm.alpha(), 0));</div>
<div class="line"><a id="l00657" name="l00657"></a><span class="lineno">  657</span>            solver.solve(<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">base_type::stoppingCondition</a>(), &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>());</div>
<div class="line"><a id="l00658" name="l00658"></a><span class="lineno">  658</span>            column(svm.alpha(),0)= problem.getUnpermutedAlpha();</div>
<div class="line"><a id="l00659" name="l00659"></a><span class="lineno">  659</span>            svm.offset(0) = computeBias(problem,dataset);</div>
<div class="line"><a id="l00660" name="l00660"></a><span class="lineno">  660</span>        }</div>
<div class="line"><a id="l00661" name="l00661"></a><span class="lineno">  661</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00662" name="l00662"></a><span class="lineno">  662</span>        {</div>
<div class="line"><a id="l00663" name="l00663"></a><span class="lineno">  663</span>            <span class="keyword">typedef</span> BoxConstrainedShrinkingProblem&lt;SVMProblemType&gt; ProblemType;</div>
<div class="line"><a id="l00664" name="l00664"></a><span class="lineno">  664</span>            ProblemType problem(svmProblem,<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00665" name="l00665"></a><span class="lineno">  665</span>            QpSolver&lt; ProblemType&gt; solver(problem);</div>
<div class="line"><a id="l00666" name="l00666"></a><span class="lineno">  666</span>            <span class="comment">// truncate the existing solution to the bounds</span></div>
<div class="line"><a id="l00667" name="l00667"></a><span class="lineno">  667</span>            RealVector <span class="keyword">const</span>&amp; reg = this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#ad06550eb45e46ff02e4789ea1b916c75">regularizationParameters</a>();</div>
<div class="line"><a id="l00668" name="l00668"></a><span class="lineno">  668</span>            <span class="keywordtype">double</span> C_minus = reg(0);</div>
<div class="line"><a id="l00669" name="l00669"></a><span class="lineno">  669</span>            <span class="keywordtype">double</span> C_plus = (reg.size() == 1) ? reg(0) : reg(1);</div>
<div class="line"><a id="l00670" name="l00670"></a><span class="lineno">  670</span>            std::size_t i=0;</div>
<div class="line"><a id="l00671" name="l00671"></a><span class="lineno">  671</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> label : dataset.labels().elements()) {</div>
<div class="line"><a id="l00672" name="l00672"></a><span class="lineno">  672</span>                <span class="keywordtype">double</span> a = svm.alpha()(i, 0);</div>
<div class="line"><a id="l00673" name="l00673"></a><span class="lineno">  673</span>                <span class="keywordflow">if</span> (label == 0) a = std::max(std::min(a, 0.0), -C_minus);</div>
<div class="line"><a id="l00674" name="l00674"></a><span class="lineno">  674</span>                <span class="keywordflow">else</span>            a = std::min(std::max(a, 0.0), C_plus);</div>
<div class="line"><a id="l00675" name="l00675"></a><span class="lineno">  675</span>                svm.alpha()(i, 0) = a;</div>
<div class="line"><a id="l00676" name="l00676"></a><span class="lineno">  676</span>                i++;</div>
<div class="line"><a id="l00677" name="l00677"></a><span class="lineno">  677</span>            }</div>
<div class="line"><a id="l00678" name="l00678"></a><span class="lineno">  678</span>            problem.setInitialSolution(blas::column(svm.alpha(), 0));</div>
<div class="line"><a id="l00679" name="l00679"></a><span class="lineno">  679</span>            solver.solve(<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">base_type::stoppingCondition</a>(), &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>());</div>
<div class="line"><a id="l00680" name="l00680"></a><span class="lineno">  680</span>            column(svm.alpha(),0) = problem.getUnpermutedAlpha();</div>
<div class="line"><a id="l00681" name="l00681"></a><span class="lineno">  681</span>        }</div>
<div class="line"><a id="l00682" name="l00682"></a><span class="lineno">  682</span>    }</div>
<div class="line"><a id="l00683" name="l00683"></a><span class="lineno">  683</span>    </div>
<div class="line"><a id="l00684" name="l00684"></a><span class="lineno">  684</span>    RealVector m_db_dParams; <span class="comment">///&lt; in the rare case that there are only bounded SVs and no free SVs, this will hold the derivative of b w.r.t. the hyperparameters. Derivative w.r.t. C is last.</span></div>
<div class="line"><a id="l00685" name="l00685"></a><span class="lineno">  685</span> </div>
<div class="line"><a id="l00686" name="l00686"></a><span class="lineno">  686</span>    <span class="keywordtype">bool</span> m_computeDerivative;</div>
<div class="line"><a id="l00687" name="l00687"></a><span class="lineno">  687</span>    <a class="code hl_enumeration" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">McSvm</a> m_McSvmType;</div>
<div class="line"><a id="l00688" name="l00688"></a><span class="lineno">  688</span> </div>
<div class="line"><a id="l00689" name="l00689"></a><span class="lineno">  689</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Problem&gt;</div>
<div class="line"><a id="l00690" name="l00690"></a><span class="lineno">  690</span>    <span class="keywordtype">double</span> computeBias(Problem <span class="keyword">const</span>&amp; problem, LabeledData&lt;InputType, unsigned int&gt; <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00691" name="l00691"></a><span class="lineno">  691</span>        std::size_t nkp = <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>();</div>
<div class="line"><a id="l00692" name="l00692"></a><span class="lineno">  692</span>        m_db_dParams.resize(nkp+1);</div>
<div class="line"><a id="l00693" name="l00693"></a><span class="lineno">  693</span>        m_db_dParams.clear();</div>
<div class="line"><a id="l00694" name="l00694"></a><span class="lineno">  694</span> </div>
<div class="line"><a id="l00695" name="l00695"></a><span class="lineno">  695</span>        std::size_t ell = problem.dimensions();</div>
<div class="line"><a id="l00696" name="l00696"></a><span class="lineno">  696</span>        <span class="keywordflow">if</span> (ell == 0) <span class="keywordflow">return</span> 0.0;</div>
<div class="line"><a id="l00697" name="l00697"></a><span class="lineno">  697</span> </div>
<div class="line"><a id="l00698" name="l00698"></a><span class="lineno">  698</span>        <span class="comment">// compute the offset from the KKT conditions</span></div>
<div class="line"><a id="l00699" name="l00699"></a><span class="lineno">  699</span>        <span class="keywordtype">double</span> lowerBound = -1e100;</div>
<div class="line"><a id="l00700" name="l00700"></a><span class="lineno">  700</span>        <span class="keywordtype">double</span> upperBound = 1e100;</div>
<div class="line"><a id="l00701" name="l00701"></a><span class="lineno">  701</span>        <span class="keywordtype">double</span> sum = 0.0;</div>
<div class="line"><a id="l00702" name="l00702"></a><span class="lineno">  702</span>        std::size_t freeVars = 0;</div>
<div class="line"><a id="l00703" name="l00703"></a><span class="lineno">  703</span>        std::size_t lower_i = 0;</div>
<div class="line"><a id="l00704" name="l00704"></a><span class="lineno">  704</span>        std::size_t upper_i = 0;</div>
<div class="line"><a id="l00705" name="l00705"></a><span class="lineno">  705</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;ell; i++)</div>
<div class="line"><a id="l00706" name="l00706"></a><span class="lineno">  706</span>        {</div>
<div class="line"><a id="l00707" name="l00707"></a><span class="lineno">  707</span>            <span class="keywordtype">double</span> value = problem.gradient(i);</div>
<div class="line"><a id="l00708" name="l00708"></a><span class="lineno">  708</span>            <span class="keywordflow">if</span> (problem.alpha(i) == problem.boxMin(i))</div>
<div class="line"><a id="l00709" name="l00709"></a><span class="lineno">  709</span>            {</div>
<div class="line"><a id="l00710" name="l00710"></a><span class="lineno">  710</span>                <span class="keywordflow">if</span> (value &gt; lowerBound) { <span class="comment">//in case of no free SVs, we are looking for the largest gradient of all alphas at the lower bound</span></div>
<div class="line"><a id="l00711" name="l00711"></a><span class="lineno">  711</span>                    lowerBound = value;</div>
<div class="line"><a id="l00712" name="l00712"></a><span class="lineno">  712</span>                    lower_i = i;</div>
<div class="line"><a id="l00713" name="l00713"></a><span class="lineno">  713</span>                }</div>
<div class="line"><a id="l00714" name="l00714"></a><span class="lineno">  714</span>            }</div>
<div class="line"><a id="l00715" name="l00715"></a><span class="lineno">  715</span>            <span class="keywordflow">else</span> <span class="keywordflow">if</span> (problem.alpha(i) == problem.boxMax(i))</div>
<div class="line"><a id="l00716" name="l00716"></a><span class="lineno">  716</span>            {</div>
<div class="line"><a id="l00717" name="l00717"></a><span class="lineno">  717</span>                <span class="keywordflow">if</span> (value &lt; upperBound) { <span class="comment">//in case of no free SVs, we are looking for the smallest gradient of all alphas at the upper bound</span></div>
<div class="line"><a id="l00718" name="l00718"></a><span class="lineno">  718</span>                    upperBound = value;</div>
<div class="line"><a id="l00719" name="l00719"></a><span class="lineno">  719</span>                    upper_i = i;</div>
<div class="line"><a id="l00720" name="l00720"></a><span class="lineno">  720</span>                }</div>
<div class="line"><a id="l00721" name="l00721"></a><span class="lineno">  721</span>            }</div>
<div class="line"><a id="l00722" name="l00722"></a><span class="lineno">  722</span>            <span class="keywordflow">else</span></div>
<div class="line"><a id="l00723" name="l00723"></a><span class="lineno">  723</span>            {</div>
<div class="line"><a id="l00724" name="l00724"></a><span class="lineno">  724</span>                sum += value;</div>
<div class="line"><a id="l00725" name="l00725"></a><span class="lineno">  725</span>                freeVars++;</div>
<div class="line"><a id="l00726" name="l00726"></a><span class="lineno">  726</span>            }</div>
<div class="line"><a id="l00727" name="l00727"></a><span class="lineno">  727</span>        }</div>
<div class="line"><a id="l00728" name="l00728"></a><span class="lineno">  728</span>        <span class="keywordflow">if</span> (freeVars &gt; 0)</div>
<div class="line"><a id="l00729" name="l00729"></a><span class="lineno">  729</span>            <span class="keywordflow">return</span> sum / freeVars;      <span class="comment">//stabilized (averaged) exact value</span></div>
<div class="line"><a id="l00730" name="l00730"></a><span class="lineno">  730</span> </div>
<div class="line"><a id="l00731" name="l00731"></a><span class="lineno">  731</span>        <span class="keywordflow">if</span>(!m_computeDerivative)</div>
<div class="line"><a id="l00732" name="l00732"></a><span class="lineno">  732</span>            <span class="keywordflow">return</span> 0.5 * (lowerBound + upperBound); <span class="comment">//best estimate</span></div>
<div class="line"><a id="l00733" name="l00733"></a><span class="lineno">  733</span> </div>
<div class="line"><a id="l00734" name="l00734"></a><span class="lineno">  734</span>        lower_i = problem.permutation(lower_i);</div>
<div class="line"><a id="l00735" name="l00735"></a><span class="lineno">  735</span>        upper_i = problem.permutation(upper_i);</div>
<div class="line"><a id="l00736" name="l00736"></a><span class="lineno">  736</span> </div>
<div class="line"><a id="l00737" name="l00737"></a><span class="lineno">  737</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a>.size() == 1, <span class="stringliteral">&quot;derivative only implemented for SVM with one C&quot;</span> );</div>
<div class="line"><a id="l00738" name="l00738"></a><span class="lineno">  738</span> </div>
<div class="line"><a id="l00739" name="l00739"></a><span class="lineno">  739</span>        <span class="comment">// We next compute the derivative of lowerBound and upperBound wrt C, in order to then get that of b wrt C.</span></div>
<div class="line"><a id="l00740" name="l00740"></a><span class="lineno">  740</span>        <span class="comment">// The equation at the foundation of this simply is g_i = y_i - \sum_j \alpha_j K_{ij} .</span></div>
<div class="line"><a id="l00741" name="l00741"></a><span class="lineno">  741</span>        <span class="keywordtype">double</span> dlower_dC = 0.0;</div>
<div class="line"><a id="l00742" name="l00742"></a><span class="lineno">  742</span>        <span class="keywordtype">double</span> dupper_dC = 0.0;</div>
<div class="line"><a id="l00743" name="l00743"></a><span class="lineno">  743</span>        <span class="comment">// At the same time, we also compute the derivative of lowerBound and upperBound wrt the kernel parameters.</span></div>
<div class="line"><a id="l00744" name="l00744"></a><span class="lineno">  744</span>        <span class="comment">// The equation at the foundation of this simply is g_i = y_i - \sum_j \alpha_j K_{ij} .</span></div>
<div class="line"><a id="l00745" name="l00745"></a><span class="lineno">  745</span>        RealVector dupper_dkernel( nkp,0 );</div>
<div class="line"><a id="l00746" name="l00746"></a><span class="lineno">  746</span>        RealVector dlower_dkernel( nkp,0 );</div>
<div class="line"><a id="l00747" name="l00747"></a><span class="lineno">  747</span>        <span class="comment">//state for eval and evalDerivative of the kernel</span></div>
<div class="line"><a id="l00748" name="l00748"></a><span class="lineno">  748</span>        boost::shared_ptr&lt;State&gt; kernelState = <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a9057a4a71b4d28febb171e09bbd22c07" title="Creates an internal state of the kernel.">createState</a>();</div>
<div class="line"><a id="l00749" name="l00749"></a><span class="lineno">  749</span>        RealVector der(nkp ); <span class="comment">//derivative storage helper</span></div>
<div class="line"><a id="l00750" name="l00750"></a><span class="lineno">  750</span>        <span class="comment">//todo: O.K.: here kernel single input derivative would be usefull</span></div>
<div class="line"><a id="l00751" name="l00751"></a><span class="lineno">  751</span>        <span class="comment">//also it can be usefull to use here real batch processing and use batches of size 1 for lower /upper</span></div>
<div class="line"><a id="l00752" name="l00752"></a><span class="lineno">  752</span>        <span class="comment">//and instead of singleInput whole batches.</span></div>
<div class="line"><a id="l00753" name="l00753"></a><span class="lineno">  753</span>        <span class="comment">//what we do is, that we use the batched input versions with batches of size one.</span></div>
<div class="line"><a id="l00754" name="l00754"></a><span class="lineno">  754</span>        <span class="keyword">typename</span> Batch&lt;InputType&gt;::type singleInput = Batch&lt;InputType&gt;::createBatch( dataset.element(0).input, 1 );</div>
<div class="line"><a id="l00755" name="l00755"></a><span class="lineno">  755</span>        <span class="keyword">typename</span> Batch&lt;InputType&gt;::type lowerInput = Batch&lt;InputType&gt;::createBatch( dataset.element(lower_i).input, 1 );</div>
<div class="line"><a id="l00756" name="l00756"></a><span class="lineno">  756</span>        <span class="keyword">typename</span> Batch&lt;InputType&gt;::type upperInput = Batch&lt;InputType&gt;::createBatch( dataset.element(upper_i).input, 1 );</div>
<div class="line"><a id="l00757" name="l00757"></a><span class="lineno">  757</span>        <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>( lowerInput, 0 ) = dataset.element(lower_i).input; <span class="comment">//copy the current input into the batch</span></div>
<div class="line"><a id="l00758" name="l00758"></a><span class="lineno">  758</span>        <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>( upperInput, 0 ) = dataset.element(upper_i).input; <span class="comment">//copy the current input into the batch</span></div>
<div class="line"><a id="l00759" name="l00759"></a><span class="lineno">  759</span>        RealMatrix one(1,1,1); <span class="comment">//weight of input</span></div>
<div class="line"><a id="l00760" name="l00760"></a><span class="lineno">  760</span>        RealMatrix result(1,1); <span class="comment">//stores the result of the call</span></div>
<div class="line"><a id="l00761" name="l00761"></a><span class="lineno">  761</span> </div>
<div class="line"><a id="l00762" name="l00762"></a><span class="lineno">  762</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;ell; i++) {</div>
<div class="line"><a id="l00763" name="l00763"></a><span class="lineno">  763</span>            <span class="keywordtype">double</span> cur_alpha = problem.alpha(problem.permutation(i));</div>
<div class="line"><a id="l00764" name="l00764"></a><span class="lineno">  764</span>            <span class="keywordflow">if</span> ( cur_alpha != 0 ) {</div>
<div class="line"><a id="l00765" name="l00765"></a><span class="lineno">  765</span>                <span class="keywordtype">int</span> cur_label = ( cur_alpha&gt;0.0 ? 1 : -1 );</div>
<div class="line"><a id="l00766" name="l00766"></a><span class="lineno">  766</span>                <a class="code hl_function" href="namespaceshark.html#a1531880b9b4076854b0b26441d353242">getBatchElement</a>( singleInput, 0 ) = dataset.element(i).input; <span class="comment">//copy the current input into the batch</span></div>
<div class="line"><a id="l00767" name="l00767"></a><span class="lineno">  767</span>                <span class="comment">// treat contributions of largest gradient at lower bound</span></div>
<div class="line"><a id="l00768" name="l00768"></a><span class="lineno">  768</span>                <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>( lowerInput, singleInput, result, *kernelState );</div>
<div class="line"><a id="l00769" name="l00769"></a><span class="lineno">  769</span>                dlower_dC += cur_label * result(0,0);</div>
<div class="line"><a id="l00770" name="l00770"></a><span class="lineno">  770</span>                <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a48557b9834bc06ccb4e005ce441904c8" title="Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the ba...">weightedParameterDerivative</a>( lowerInput, singleInput,one, *kernelState, der );</div>
<div class="line"><a id="l00771" name="l00771"></a><span class="lineno">  771</span>                <span class="keywordflow">for</span> ( std::size_t k=0; k&lt;nkp; k++ ) {</div>
<div class="line"><a id="l00772" name="l00772"></a><span class="lineno">  772</span>                    dlower_dkernel(k) += cur_label * der(k);</div>
<div class="line"><a id="l00773" name="l00773"></a><span class="lineno">  773</span>                }</div>
<div class="line"><a id="l00774" name="l00774"></a><span class="lineno">  774</span>                <span class="comment">// treat contributions of smallest gradient at upper bound</span></div>
<div class="line"><a id="l00775" name="l00775"></a><span class="lineno">  775</span>                <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>( upperInput, singleInput,result, *kernelState );</div>
<div class="line"><a id="l00776" name="l00776"></a><span class="lineno">  776</span>                dupper_dC += cur_label * result(0,0);</div>
<div class="line"><a id="l00777" name="l00777"></a><span class="lineno">  777</span>                <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a48557b9834bc06ccb4e005ce441904c8" title="Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the ba...">weightedParameterDerivative</a>( upperInput, singleInput, one, *kernelState, der );</div>
<div class="line"><a id="l00778" name="l00778"></a><span class="lineno">  778</span>                <span class="keywordflow">for</span> ( std::size_t k=0; k&lt;nkp; k++ ) {</div>
<div class="line"><a id="l00779" name="l00779"></a><span class="lineno">  779</span>                    dupper_dkernel(k) += cur_label * der(k);</div>
<div class="line"><a id="l00780" name="l00780"></a><span class="lineno">  780</span>                }</div>
<div class="line"><a id="l00781" name="l00781"></a><span class="lineno">  781</span>            }</div>
<div class="line"><a id="l00782" name="l00782"></a><span class="lineno">  782</span>        }</div>
<div class="line"><a id="l00783" name="l00783"></a><span class="lineno">  783</span>        <span class="comment">// assign final values to derivative of b wrt hyperparameters</span></div>
<div class="line"><a id="l00784" name="l00784"></a><span class="lineno">  784</span>        m_db_dParams( nkp ) = -0.5 * ( dlower_dC + dupper_dC );</div>
<div class="line"><a id="l00785" name="l00785"></a><span class="lineno">  785</span>        <span class="keywordflow">for</span> ( std::size_t k=0; k&lt;nkp; k++ ) {</div>
<div class="line"><a id="l00786" name="l00786"></a><span class="lineno">  786</span>            m_db_dParams(k) = -0.5 * this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>() * ( dlower_dkernel(k) + dupper_dkernel(k) );</div>
<div class="line"><a id="l00787" name="l00787"></a><span class="lineno">  787</span>        }</div>
<div class="line"><a id="l00788" name="l00788"></a><span class="lineno">  788</span>        <span class="keywordflow">if</span> ( <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa3e2f2db97947d244213f63093a08878" title="Is log(C) stored internally as a parameter instead of C? If yes, then we get rid of the constraint C ...">base_type::m_unconstrained</a> ) {</div>
<div class="line"><a id="l00789" name="l00789"></a><span class="lineno">  789</span>            m_db_dParams( nkp ) *= this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>();</div>
<div class="line"><a id="l00790" name="l00790"></a><span class="lineno">  790</span>        }</div>
<div class="line"><a id="l00791" name="l00791"></a><span class="lineno">  791</span>        </div>
<div class="line"><a id="l00792" name="l00792"></a><span class="lineno">  792</span>        <span class="keywordflow">return</span> 0.5 * (lowerBound + upperBound); <span class="comment">//best estimate</span></div>
<div class="line"><a id="l00793" name="l00793"></a><span class="lineno">  793</span>    }</div>
<div class="line"><a id="l00794" name="l00794"></a><span class="lineno">  794</span>};</div>
</div>
<div class="line"><a id="l00795" name="l00795"></a><span class="lineno">  795</span> </div>
<div class="line"><a id="l00796" name="l00796"></a><span class="lineno">  796</span> </div>
<div class="line"><a id="l00797" name="l00797"></a><span class="lineno">  797</span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="foldopen" id="foldopen00798" data-start="{" data-end="};">
<div class="line"><a id="l00798" name="l00798"></a><span class="lineno"><a class="line" href="classshark_1_1_linear_c_svm_trainer.html">  798</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_linear_c_svm_trainer.html">LinearCSvmTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">AbstractLinearSvmTrainer</a>&lt;InputType&gt;</div>
<div class="line"><a id="l00799" name="l00799"></a><span class="lineno">  799</span>{</div>
<div class="line"><a id="l00800" name="l00800"></a><span class="lineno">  800</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00801" name="l00801"></a><span class="lineno">  801</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">AbstractLinearSvmTrainer&lt;InputType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">base_type</a>;</div>
<div class="line"><a id="l00802" name="l00802"></a><span class="lineno">  802</span> </div>
<div class="foldopen" id="foldopen00803" data-start="{" data-end="}">
<div class="line"><a id="l00803" name="l00803"></a><span class="lineno"><a class="line" href="classshark_1_1_linear_c_svm_trainer.html#a8677b6f19ea68f188cbb249edc895e9c">  803</a></span>    <a class="code hl_function" href="classshark_1_1_linear_c_svm_trainer.html#a8677b6f19ea68f188cbb249edc895e9c">LinearCSvmTrainer</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">C</a>, <span class="keywordtype">bool</span> offset, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>) </div>
<div class="line"><a id="l00804" name="l00804"></a><span class="lineno">  804</span>    : <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">AbstractLinearSvmTrainer</a>&lt;<a class="code hl_typedef" href="classshark_1_1_abstract_trainer.html#a0cfa7cdd27b8bb162e64188095f8fa71">InputType</a>&gt;(<a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">C</a>, offset, unconstrained){}</div>
</div>
<div class="line"><a id="l00805" name="l00805"></a><span class="lineno">  805</span><span class="comment"></span> </div>
<div class="line"><a id="l00806" name="l00806"></a><span class="lineno">  806</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00807" data-start="{" data-end="}">
<div class="line"><a id="l00807" name="l00807"></a><span class="lineno"><a class="line" href="classshark_1_1_linear_c_svm_trainer.html#ab775e6b9f43525fcf0cae67bbdc58914">  807</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_linear_c_svm_trainer.html#ab775e6b9f43525fcf0cae67bbdc58914" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00808" name="l00808"></a><span class="lineno">  808</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;LinearCSvmTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00809" name="l00809"></a><span class="lineno">  809</span>    <span class="comment"></span></div>
<div class="line"><a id="l00810" name="l00810"></a><span class="lineno">  810</span><span class="comment">    /// \brief sets the type of the multi-class svm used</span></div>
<div class="foldopen" id="foldopen00811" data-start="{" data-end="}">
<div class="line"><a id="l00811" name="l00811"></a><span class="lineno"><a class="line" href="classshark_1_1_linear_c_svm_trainer.html#a7de8ad805e0b63d3eca9412d8b21b578">  811</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_linear_c_svm_trainer.html#a7de8ad805e0b63d3eca9412d8b21b578" title="sets the type of the multi-class svm used">setMcSvmType</a>(<a class="code hl_enumeration" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">McSvm</a> type){</div>
<div class="line"><a id="l00812" name="l00812"></a><span class="lineno">  812</span>        m_McSvmType = type;</div>
<div class="line"><a id="l00813" name="l00813"></a><span class="lineno">  813</span>    }</div>
</div>
<div class="line"><a id="l00814" name="l00814"></a><span class="lineno">  814</span> </div>
<div class="foldopen" id="foldopen00815" data-start="{" data-end="}">
<div class="line"><a id="l00815" name="l00815"></a><span class="lineno"><a class="line" href="classshark_1_1_linear_c_svm_trainer.html#abf7128243c28edc04a01f157593ece98">  815</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_linear_c_svm_trainer.html#abf7128243c28edc04a01f157593ece98">train</a>(<a class="code hl_class" href="classshark_1_1_linear_classifier.html" title="Basic linear classifier.">LinearClassifier&lt;InputType&gt;</a>&amp; model, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset)</div>
<div class="line"><a id="l00816" name="l00816"></a><span class="lineno">  816</span>    {</div>
<div class="line"><a id="l00817" name="l00817"></a><span class="lineno">  817</span>        std::size_t classes = <a class="code hl_function" href="group__shark__globals.html#ga1fee3b5830ae11a78109e8c0265c6569" title="Return the number of classes of a set of class labels with unsigned int label encoding.">numberOfClasses</a>(dataset);</div>
<div class="line"><a id="l00818" name="l00818"></a><span class="lineno">  818</span>        <span class="keywordflow">if</span>(classes == 2){</div>
<div class="line"><a id="l00819" name="l00819"></a><span class="lineno">  819</span>            trainBinary(model,dataset);</div>
<div class="line"><a id="l00820" name="l00820"></a><span class="lineno">  820</span>            <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00821" name="l00821"></a><span class="lineno">  821</span>        }</div>
<div class="line"><a id="l00822" name="l00822"></a><span class="lineno">  822</span>        <span class="keywordflow">switch</span> (m_McSvmType){</div>
<div class="line"><a id="l00823" name="l00823"></a><span class="lineno">  823</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a0a9b52fb6605edc74fd7d5359f34477e">McSvm::WW</a>:</div>
<div class="line"><a id="l00824" name="l00824"></a><span class="lineno">  824</span>                trainMc&lt;QpMcLinearWW&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00825" name="l00825"></a><span class="lineno">  825</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00826" name="l00826"></a><span class="lineno">  826</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a8d7e99c73cd5a10adaaf4c9f9a520368">McSvm::CS</a>:</div>
<div class="line"><a id="l00827" name="l00827"></a><span class="lineno">  827</span>                trainMc&lt;QpMcLinearCS&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00828" name="l00828"></a><span class="lineno">  828</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00829" name="l00829"></a><span class="lineno">  829</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a69120030cbf53ae8224b9b4865ab3945">McSvm::LLW</a>:</div>
<div class="line"><a id="l00830" name="l00830"></a><span class="lineno">  830</span>                trainMc&lt;QpMcLinearLLW&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00831" name="l00831"></a><span class="lineno">  831</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00832" name="l00832"></a><span class="lineno">  832</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a292f535a3b6fe8853df2f03c8ed890a1">McSvm::ATM</a>:</div>
<div class="line"><a id="l00833" name="l00833"></a><span class="lineno">  833</span>                trainMc&lt;QpMcLinearATM&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00834" name="l00834"></a><span class="lineno">  834</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00835" name="l00835"></a><span class="lineno">  835</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a600d2c32c47d0285e1df97492ed3bd35">McSvm::ATS</a>:</div>
<div class="line"><a id="l00836" name="l00836"></a><span class="lineno">  836</span>                trainMc&lt;QpMcLinearATS&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00837" name="l00837"></a><span class="lineno">  837</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00838" name="l00838"></a><span class="lineno">  838</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a6fb4f22992a0d164b77267fde5477248">McSvm::ADM</a>:</div>
<div class="line"><a id="l00839" name="l00839"></a><span class="lineno">  839</span>                trainMc&lt;QpMcLinearADM&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00840" name="l00840"></a><span class="lineno">  840</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00841" name="l00841"></a><span class="lineno">  841</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112ada27331778aba3d85176f2c76f49bcc8">McSvm::MMR</a>:</div>
<div class="line"><a id="l00842" name="l00842"></a><span class="lineno">  842</span>                trainMc&lt;QpMcLinearMMR&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00843" name="l00843"></a><span class="lineno">  843</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00844" name="l00844"></a><span class="lineno">  844</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a354624ba01e474ac153a71c5e0d3b266">McSvm::ReinforcedSvm</a>:</div>
<div class="line"><a id="l00845" name="l00845"></a><span class="lineno">  845</span>                trainMc&lt;QpMcLinearReinforced&lt;InputType&gt; &gt;(model,dataset,classes);</div>
<div class="line"><a id="l00846" name="l00846"></a><span class="lineno">  846</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00847" name="l00847"></a><span class="lineno">  847</span>            <span class="keywordflow">case</span> <a class="code hl_enumvalue" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112a586ba478b7ebec665f3df120799b6c2e">McSvm::OVA</a>:<span class="comment">//OVA is a special case and implemented here</span></div>
<div class="line"><a id="l00848" name="l00848"></a><span class="lineno">  848</span>                trainOVA(model,dataset,classes);</div>
<div class="line"><a id="l00849" name="l00849"></a><span class="lineno">  849</span>            <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00850" name="l00850"></a><span class="lineno">  850</span>        }</div>
<div class="line"><a id="l00851" name="l00851"></a><span class="lineno">  851</span>    }</div>
</div>
<div class="line"><a id="l00852" name="l00852"></a><span class="lineno">  852</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00853" name="l00853"></a><span class="lineno">  853</span>    <a class="code hl_enumeration" href="namespaceshark.html#acd7a165c1049db7d676d77e708f53112">McSvm</a> m_McSvmType;</div>
<div class="line"><a id="l00854" name="l00854"></a><span class="lineno">  854</span> </div>
<div class="line"><a id="l00855" name="l00855"></a><span class="lineno">  855</span>    <span class="keywordtype">void</span> trainBinary(<a class="code hl_class" href="classshark_1_1_linear_classifier.html" title="Basic linear classifier.">LinearClassifier&lt;InputType&gt;</a>&amp; model, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset)</div>
<div class="line"><a id="l00856" name="l00856"></a><span class="lineno">  856</span>    {</div>
<div class="line"><a id="l00857" name="l00857"></a><span class="lineno">  857</span>        std::size_t dim = <a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(dataset);</div>
<div class="line"><a id="l00858" name="l00858"></a><span class="lineno">  858</span>        <a class="code hl_class" href="classshark_1_1_qp_box_linear.html" title="Quadratic program solver for box-constrained problems with linear kernel.">QpBoxLinear&lt;InputType&gt;</a> solver(dataset, dim);</div>
<div class="line"><a id="l00859" name="l00859"></a><span class="lineno">  859</span>        solver.solve(</div>
<div class="line"><a id="l00860" name="l00860"></a><span class="lineno">  860</span>                <a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">base_type::C</a>(),</div>
<div class="line"><a id="l00861" name="l00861"></a><span class="lineno">  861</span>                0.0,</div>
<div class="line"><a id="l00862" name="l00862"></a><span class="lineno">  862</span>                <a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">QpConfig::stoppingCondition</a>(),</div>
<div class="line"><a id="l00863" name="l00863"></a><span class="lineno">  863</span>                &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">QpConfig::solutionProperties</a>(),</div>
<div class="line"><a id="l00864" name="l00864"></a><span class="lineno">  864</span>                <a class="code hl_function" href="classshark_1_1_qp_config.html#a71328214090e442c9fee46103868b0ca" title="Verbosity level of the solver.">QpConfig::verbosity</a>() &gt; 0);</div>
<div class="line"><a id="l00865" name="l00865"></a><span class="lineno">  865</span>        </div>
<div class="line"><a id="l00866" name="l00866"></a><span class="lineno">  866</span>        <span class="keywordflow">if</span>(!this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#a5d00f5cfe51a496a1511219d12a4c054">trainOffset</a>()){</div>
<div class="line"><a id="l00867" name="l00867"></a><span class="lineno">  867</span>            RealMatrix w(1, dim, 0.0);</div>
<div class="line"><a id="l00868" name="l00868"></a><span class="lineno">  868</span>            row(w,0) = solver.solutionWeightVector();</div>
<div class="line"><a id="l00869" name="l00869"></a><span class="lineno">  869</span>            model.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().setStructure(w);</div>
<div class="line"><a id="l00870" name="l00870"></a><span class="lineno">  870</span>            <span class="keywordflow">return</span>;</div>
<div class="line"><a id="l00871" name="l00871"></a><span class="lineno">  871</span>        }</div>
<div class="line"><a id="l00872" name="l00872"></a><span class="lineno">  872</span>        </div>
<div class="line"><a id="l00873" name="l00873"></a><span class="lineno">  873</span>        <span class="keywordtype">double</span> offset = 0;</div>
<div class="line"><a id="l00874" name="l00874"></a><span class="lineno">  874</span>        <span class="keywordtype">double</span> stepSize = 0.1;</div>
<div class="line"><a id="l00875" name="l00875"></a><span class="lineno">  875</span>        <span class="keywordtype">double</span> grad = solver.offsetGradient();</div>
<div class="line"><a id="l00876" name="l00876"></a><span class="lineno">  876</span>        <span class="keywordflow">while</span>(stepSize &gt; 0.1*<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">QpConfig::stoppingCondition</a>().minAccuracy){</div>
<div class="line"><a id="l00877" name="l00877"></a><span class="lineno">  877</span>            offset+= (grad &lt; 0? -stepSize:stepSize);</div>
<div class="line"><a id="l00878" name="l00878"></a><span class="lineno">  878</span>            solver.setOffset(offset);</div>
<div class="line"><a id="l00879" name="l00879"></a><span class="lineno">  879</span>            solver.solve(</div>
<div class="line"><a id="l00880" name="l00880"></a><span class="lineno">  880</span>                <a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">base_type::C</a>(),</div>
<div class="line"><a id="l00881" name="l00881"></a><span class="lineno">  881</span>                0.0,</div>
<div class="line"><a id="l00882" name="l00882"></a><span class="lineno">  882</span>                <a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">QpConfig::stoppingCondition</a>(),</div>
<div class="line"><a id="l00883" name="l00883"></a><span class="lineno">  883</span>                &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">QpConfig::solutionProperties</a>(),</div>
<div class="line"><a id="l00884" name="l00884"></a><span class="lineno">  884</span>                <a class="code hl_function" href="classshark_1_1_qp_config.html#a71328214090e442c9fee46103868b0ca" title="Verbosity level of the solver.">QpConfig::verbosity</a>() &gt; 0);</div>
<div class="line"><a id="l00885" name="l00885"></a><span class="lineno">  885</span>            <span class="keywordtype">double</span> newGrad = solver.offsetGradient();</div>
<div class="line"><a id="l00886" name="l00886"></a><span class="lineno">  886</span>            <span class="keywordflow">if</span>(newGrad == 0)</div>
<div class="line"><a id="l00887" name="l00887"></a><span class="lineno">  887</span>                <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00888" name="l00888"></a><span class="lineno">  888</span>            <span class="keywordflow">if</span>(newGrad*grad &lt; 0)</div>
<div class="line"><a id="l00889" name="l00889"></a><span class="lineno">  889</span>                stepSize *= 0.5;</div>
<div class="line"><a id="l00890" name="l00890"></a><span class="lineno">  890</span>            <span class="keywordflow">else</span></div>
<div class="line"><a id="l00891" name="l00891"></a><span class="lineno">  891</span>                stepSize *= 1.6;</div>
<div class="line"><a id="l00892" name="l00892"></a><span class="lineno">  892</span>            grad = newGrad;</div>
<div class="line"><a id="l00893" name="l00893"></a><span class="lineno">  893</span>        }</div>
<div class="line"><a id="l00894" name="l00894"></a><span class="lineno">  894</span>        </div>
<div class="line"><a id="l00895" name="l00895"></a><span class="lineno">  895</span>        RealMatrix w(1, dim, 0.0);</div>
<div class="line"><a id="l00896" name="l00896"></a><span class="lineno">  896</span>        noalias(row(w,0)) = solver.solutionWeightVector();</div>
<div class="line"><a id="l00897" name="l00897"></a><span class="lineno">  897</span>        model.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().setStructure(w,RealVector(1,offset));</div>
<div class="line"><a id="l00898" name="l00898"></a><span class="lineno">  898</span>        </div>
<div class="line"><a id="l00899" name="l00899"></a><span class="lineno">  899</span>    }</div>
<div class="line"><a id="l00900" name="l00900"></a><span class="lineno">  900</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Solver&gt;</div>
<div class="line"><a id="l00901" name="l00901"></a><span class="lineno">  901</span>    <span class="keywordtype">void</span> trainMc(LinearClassifier&lt;InputType&gt;&amp; model, LabeledData&lt;InputType, unsigned int&gt; <span class="keyword">const</span>&amp; dataset, std::size_t classes){</div>
<div class="line"><a id="l00902" name="l00902"></a><span class="lineno">  902</span>        std::size_t dim = <a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(dataset);</div>
<div class="line"><a id="l00903" name="l00903"></a><span class="lineno">  903</span> </div>
<div class="line"><a id="l00904" name="l00904"></a><span class="lineno">  904</span>        Solver solver(dataset, dim, classes);</div>
<div class="line"><a id="l00905" name="l00905"></a><span class="lineno">  905</span>        RealMatrix w = solver.solve(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">C</a>(), this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">stoppingCondition</a>(), &amp;this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">solutionProperties</a>(), this-&gt;<a class="code hl_function" href="classshark_1_1_qp_config.html#a71328214090e442c9fee46103868b0ca" title="Verbosity level of the solver.">verbosity</a>() &gt; 0);</div>
<div class="line"><a id="l00906" name="l00906"></a><span class="lineno">  906</span>        model.decisionFunction().setStructure(w);</div>
<div class="line"><a id="l00907" name="l00907"></a><span class="lineno">  907</span>    }</div>
<div class="line"><a id="l00908" name="l00908"></a><span class="lineno">  908</span>    </div>
<div class="line"><a id="l00909" name="l00909"></a><span class="lineno">  909</span>    <span class="keywordtype">void</span> trainOVA(LinearClassifier&lt;InputType&gt;&amp; model, <span class="keyword">const</span> LabeledData&lt;InputType, unsigned int&gt;&amp; dataset, std::size_t classes)</div>
<div class="line"><a id="l00910" name="l00910"></a><span class="lineno">  910</span>    {</div>
<div class="line"><a id="l00911" name="l00911"></a><span class="lineno">  911</span>        <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.type = <a class="code hl_enumvalue" href="namespaceshark.html#a2d5e9a415ae7e8dd41caf883e1873540ab544fb3e76bdbaf78448f2416367ccc7">QpNone</a>;</div>
<div class="line"><a id="l00912" name="l00912"></a><span class="lineno">  912</span>        <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a754cf16ef14ec337a626ab31c11ac444" title="typically the maximal KKT violation">accuracy</a> = 0.0;</div>
<div class="line"><a id="l00913" name="l00913"></a><span class="lineno">  913</span>        <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#aa1b7fb15931dfebb70364b0bf949fa15" title="number of decomposition iterations">iterations</a> = 0;</div>
<div class="line"><a id="l00914" name="l00914"></a><span class="lineno">  914</span>        <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#aa3b4e5dcba8e39ee858c6ea36c8879b3" title="value of the objective function">value</a> = 0.0;</div>
<div class="line"><a id="l00915" name="l00915"></a><span class="lineno">  915</span>        <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a965ff3df7a7c9ae101b0b06e82921c91" title="training time">seconds</a> = 0.0;</div>
<div class="line"><a id="l00916" name="l00916"></a><span class="lineno">  916</span> </div>
<div class="line"><a id="l00917" name="l00917"></a><span class="lineno">  917</span>        std::size_t dim = <a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(dataset);</div>
<div class="line"><a id="l00918" name="l00918"></a><span class="lineno">  918</span>        RealMatrix w(classes, dim);</div>
<div class="line"><a id="l00919" name="l00919"></a><span class="lineno">  919</span>        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> c=0; c&lt;classes; c++)</div>
<div class="line"><a id="l00920" name="l00920"></a><span class="lineno">  920</span>        {</div>
<div class="line"><a id="l00921" name="l00921"></a><span class="lineno">  921</span>            LabeledData&lt;InputType, unsigned int&gt; bindata = <a class="code hl_function" href="group__shark__globals.html#gac1a150d7458195ce9212917b4956a4b7" title="Construct a binary (two-class) one-versus-rest problem from a multi-class problem.">oneVersusRestProblem</a>(dataset, c);</div>
<div class="line"><a id="l00922" name="l00922"></a><span class="lineno">  922</span>            QpBoxLinear&lt;InputType&gt; solver(bindata, dim);</div>
<div class="line"><a id="l00923" name="l00923"></a><span class="lineno">  923</span>            QpSolutionProperties prop;</div>
<div class="line"><a id="l00924" name="l00924"></a><span class="lineno">  924</span>            solver.solve(this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">C</a>(), 0.0, <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a5032921be220d76232e7db3db3ef5225" title="conditions for when to stop the QP solver">base_type::m_stoppingcondition</a>, &amp;prop, <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#ad6f54a3b58cd6a2e1774d5decf8fcc79" title="verbosity level (currently unused)">base_type::m_verbosity</a> &gt; 0);</div>
<div class="line"><a id="l00925" name="l00925"></a><span class="lineno">  925</span>            noalias(row(w, c)) = solver.solutionWeightVector();</div>
<div class="line"><a id="l00926" name="l00926"></a><span class="lineno">  926</span>            <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#aa1b7fb15931dfebb70364b0bf949fa15" title="number of decomposition iterations">iterations</a> += prop.iterations;</div>
<div class="line"><a id="l00927" name="l00927"></a><span class="lineno">  927</span>            <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a965ff3df7a7c9ae101b0b06e82921c91" title="training time">seconds</a> += prop.seconds;</div>
<div class="line"><a id="l00928" name="l00928"></a><span class="lineno">  928</span>            <a class="code hl_variable" href="classshark_1_1_abstract_linear_svm_trainer.html#a994efb841504c52e509d0bac04f41fb2" title="properties of the approximate solution found by the solver">base_type::m_solutionproperties</a>.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a754cf16ef14ec337a626ab31c11ac444" title="typically the maximal KKT violation">accuracy</a> = std::max(<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>().accuracy, prop.<a class="code hl_variable" href="structshark_1_1_qp_solution_properties.html#a754cf16ef14ec337a626ab31c11ac444" title="typically the maximal KKT violation">accuracy</a>);</div>
<div class="line"><a id="l00929" name="l00929"></a><span class="lineno">  929</span>        }</div>
<div class="line"><a id="l00930" name="l00930"></a><span class="lineno">  930</span>        model.decisionFunction().setStructure(w);</div>
<div class="line"><a id="l00931" name="l00931"></a><span class="lineno">  931</span>    }</div>
<div class="line"><a id="l00932" name="l00932"></a><span class="lineno">  932</span>};</div>
</div>
<div class="line"><a id="l00933" name="l00933"></a><span class="lineno">  933</span> </div>
<div class="line"><a id="l00934" name="l00934"></a><span class="lineno">  934</span> </div>
<div class="line"><a id="l00935" name="l00935"></a><span class="lineno">  935</span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> CacheType = <span class="keywordtype">float</span>&gt;</div>
<div class="foldopen" id="foldopen00936" data-start="{" data-end="};">
<div class="line"><a id="l00936" name="l00936"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html">  936</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_squared_hinge_c_svm_trainer.html">SquaredHingeCSvmTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer</a>&lt;InputType, unsigned int&gt;</div>
<div class="line"><a id="l00937" name="l00937"></a><span class="lineno">  937</span>{</div>
<div class="line"><a id="l00938" name="l00938"></a><span class="lineno">  938</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00939" name="l00939"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#affbfafc782f6b2e49bf755b51ce36711">  939</a></span>    <span class="keyword">typedef</span> CacheType <a class="code hl_typedef" href="classshark_1_1_squared_hinge_c_svm_trainer.html#affbfafc782f6b2e49bf755b51ce36711">QpFloatType</a>;</div>
<div class="line"><a id="l00940" name="l00940"></a><span class="lineno">  940</span> </div>
<div class="line"><a id="l00941" name="l00941"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a0a8c323de0c8ba22a762902be6628305">  941</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_regularized_kernel_matrix.html" title="Kernel Gram matrix with modified diagonal.">RegularizedKernelMatrix&lt;InputType, QpFloatType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a0a8c323de0c8ba22a762902be6628305">KernelMatrixType</a>;</div>
<div class="line"><a id="l00942" name="l00942"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#aaa0072f82e11ef975525ce5dff730d3f">  942</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_cached_matrix.html" title="Efficient quadratic matrix cache.">CachedMatrix&lt; KernelMatrixType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_squared_hinge_c_svm_trainer.html#aaa0072f82e11ef975525ce5dff730d3f">CachedMatrixType</a>;</div>
<div class="line"><a id="l00943" name="l00943"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a9fe861ad7ccd5a4f2d89732f010c26c4">  943</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_precomputed_matrix.html" title="Precomputed version of a matrix for quadratic programming.">PrecomputedMatrix&lt; KernelMatrixType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a9fe861ad7ccd5a4f2d89732f010c26c4">PrecomputedMatrixType</a>;</div>
<div class="line"><a id="l00944" name="l00944"></a><span class="lineno">  944</span> </div>
<div class="line"><a id="l00945" name="l00945"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#ad69153c70a470b6e40b73e746c578fa9">  945</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_model.html" title="Base class for all Models.">AbstractModel&lt;InputType, RealVector&gt;</a> <a class="code hl_typedef" href="classshark_1_1_squared_hinge_c_svm_trainer.html#ad69153c70a470b6e40b73e746c578fa9">ModelType</a>;</div>
<div class="line"><a id="l00946" name="l00946"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a9de0c374813d406e795c3612f16f7148">  946</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a9de0c374813d406e795c3612f16f7148">KernelType</a>;</div>
<div class="line"><a id="l00947" name="l00947"></a><span class="lineno">  947</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer&lt;InputType, unsigned int&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>;</div>
<div class="line"><a id="l00948" name="l00948"></a><span class="lineno">  948</span><span class="comment"></span> </div>
<div class="line"><a id="l00949" name="l00949"></a><span class="lineno">  949</span><span class="comment">    //! Constructor</span></div>
<div class="line"><a id="l00950" name="l00950"></a><span class="lineno">  950</span><span class="comment">    //! \param  kernel         kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00951" name="l00951"></a><span class="lineno">  951</span><span class="comment">    //! \param  C              regularization parameter - always the &#39;true&#39; value of C, even when unconstrained is set</span></div>
<div class="line"><a id="l00952" name="l00952"></a><span class="lineno">  952</span><span class="comment">    //! \param  unconstrained  when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver??</span></div>
<div class="foldopen" id="foldopen00953" data-start="{" data-end="}">
<div class="line"><a id="l00953" name="l00953"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a9448aa8e5bffd616d9a5ba2955b432ed">  953</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a9448aa8e5bffd616d9a5ba2955b432ed">SquaredHingeCSvmTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>)</div>
<div class="line"><a id="l00954" name="l00954"></a><span class="lineno">  954</span>    : <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, unconstrained)</div>
<div class="line"><a id="l00955" name="l00955"></a><span class="lineno">  955</span>    { }</div>
</div>
<div class="line"><a id="l00956" name="l00956"></a><span class="lineno">  956</span>    <span class="comment"></span></div>
<div class="line"><a id="l00957" name="l00957"></a><span class="lineno">  957</span><span class="comment">    //! Constructor</span></div>
<div class="line"><a id="l00958" name="l00958"></a><span class="lineno">  958</span><span class="comment">    //! \param  kernel         kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00959" name="l00959"></a><span class="lineno">  959</span><span class="comment">    //! \param  negativeC   regularization parameter of the negative class (label 0)</span></div>
<div class="line"><a id="l00960" name="l00960"></a><span class="lineno">  960</span><span class="comment">    //! \param  positiveC    regularization parameter of the positive class (label 1)</span></div>
<div class="line"><a id="l00961" name="l00961"></a><span class="lineno">  961</span><span class="comment">    //! \param  unconstrained  when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?</span></div>
<div class="foldopen" id="foldopen00962" data-start="{" data-end="}">
<div class="line"><a id="l00962" name="l00962"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#aa4a1b4bcb8ddcd53de1928109d29e39a">  962</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_squared_hinge_c_svm_trainer.html#aa4a1b4bcb8ddcd53de1928109d29e39a">SquaredHingeCSvmTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <span class="keywordtype">double</span> negativeC, <span class="keywordtype">double</span> positiveC, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>)</div>
<div class="line"><a id="l00963" name="l00963"></a><span class="lineno">  963</span>    : <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>,negativeC, positiveC, unconstrained)</div>
<div class="line"><a id="l00964" name="l00964"></a><span class="lineno">  964</span>    { }</div>
</div>
<div class="line"><a id="l00965" name="l00965"></a><span class="lineno">  965</span><span class="comment"></span> </div>
<div class="line"><a id="l00966" name="l00966"></a><span class="lineno">  966</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00967" data-start="{" data-end="}">
<div class="line"><a id="l00967" name="l00967"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a71ab3305c3272052460e3e163cd0c6e4">  967</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a71ab3305c3272052460e3e163cd0c6e4" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00968" name="l00968"></a><span class="lineno">  968</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;SquaredHingeCSvmTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00969" name="l00969"></a><span class="lineno">  969</span><span class="comment"></span> </div>
<div class="line"><a id="l00970" name="l00970"></a><span class="lineno">  970</span><span class="comment">    /// \brief Train the C-SVM.</span></div>
<div class="foldopen" id="foldopen00971" data-start="{" data-end="}">
<div class="line"><a id="l00971" name="l00971"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a039491eb212c684d4585db0132545a85">  971</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_squared_hinge_c_svm_trainer.html#a039491eb212c684d4585db0132545a85" title="Train the C-SVM.">train</a>(<a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;InputType&gt;</a>&amp; svm, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset)</div>
<div class="line"><a id="l00972" name="l00972"></a><span class="lineno">  972</span>    {       </div>
<div class="line"><a id="l00973" name="l00973"></a><span class="lineno">  973</span>        svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().setStructure(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>,dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(),this-&gt;m_trainOffset);</div>
<div class="line"><a id="l00974" name="l00974"></a><span class="lineno">  974</span>        </div>
<div class="line"><a id="l00975" name="l00975"></a><span class="lineno">  975</span>        RealVector diagonalModifier(dataset.<a class="code hl_function" href="group__shark__globals.html#ga5333445992cd6b14392cd80a1ab5403c" title="Returns the total number of elements.">numberOfElements</a>(),0.5/<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a>(0));</div>
<div class="line"><a id="l00976" name="l00976"></a><span class="lineno">  976</span>        <span class="keywordflow">if</span>(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a>.size() != 1){</div>
<div class="line"><a id="l00977" name="l00977"></a><span class="lineno">  977</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != diagonalModifier.size();++i){</div>
<div class="line"><a id="l00978" name="l00978"></a><span class="lineno">  978</span>                diagonalModifier(i) = 0.5/<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa5c86718ae82edb7660fe5769ebc5b0f" title="Vector of regularization parameters.">base_type::m_regularizers</a>(dataset.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(i).label);</div>
<div class="line"><a id="l00979" name="l00979"></a><span class="lineno">  979</span>            }</div>
<div class="line"><a id="l00980" name="l00980"></a><span class="lineno">  980</span>        }</div>
<div class="line"><a id="l00981" name="l00981"></a><span class="lineno">  981</span>        </div>
<div class="line"><a id="l00982" name="l00982"></a><span class="lineno">  982</span>        <a class="code hl_class" href="classshark_1_1_regularized_kernel_matrix.html" title="Kernel Gram matrix with modified diagonal.">KernelMatrixType</a> km(*<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(),diagonalModifier);</div>
<div class="line"><a id="l00983" name="l00983"></a><span class="lineno">  983</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">QpConfig::precomputeKernel</a>())</div>
<div class="line"><a id="l00984" name="l00984"></a><span class="lineno">  984</span>        {</div>
<div class="line"><a id="l00985" name="l00985"></a><span class="lineno">  985</span>            <a class="code hl_class" href="classshark_1_1_precomputed_matrix.html" title="Precomputed version of a matrix for quadratic programming.">PrecomputedMatrixType</a> matrix(&amp;km);</div>
<div class="line"><a id="l00986" name="l00986"></a><span class="lineno">  986</span>            optimize(svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>(),matrix,diagonalModifier,dataset);</div>
<div class="line"><a id="l00987" name="l00987"></a><span class="lineno">  987</span>        }</div>
<div class="line"><a id="l00988" name="l00988"></a><span class="lineno">  988</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00989" name="l00989"></a><span class="lineno">  989</span>        {</div>
<div class="line"><a id="l00990" name="l00990"></a><span class="lineno">  990</span>            <a class="code hl_class" href="classshark_1_1_cached_matrix.html" title="Efficient quadratic matrix cache.">CachedMatrixType</a> matrix(&amp;km);</div>
<div class="line"><a id="l00991" name="l00991"></a><span class="lineno">  991</span>            optimize(svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>(),matrix,diagonalModifier,dataset);</div>
<div class="line"><a id="l00992" name="l00992"></a><span class="lineno">  992</span>        }</div>
<div class="line"><a id="l00993" name="l00993"></a><span class="lineno">  993</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> = km.<a class="code hl_function" href="classshark_1_1_regularized_kernel_matrix.html#ae18e8a97284551effcabcbf2a4bac945" title="query the kernel access counter">getAccessCount</a>();</div>
<div class="line"><a id="l00994" name="l00994"></a><span class="lineno">  994</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">base_type::sparsify</a>()) svm.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().sparsify();</div>
<div class="line"><a id="l00995" name="l00995"></a><span class="lineno">  995</span> </div>
<div class="line"><a id="l00996" name="l00996"></a><span class="lineno">  996</span>    }</div>
</div>
<div class="line"><a id="l00997" name="l00997"></a><span class="lineno">  997</span> </div>
<div class="line"><a id="l00998" name="l00998"></a><span class="lineno">  998</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00999" name="l00999"></a><span class="lineno">  999</span>    </div>
<div class="line"><a id="l01000" name="l01000"></a><span class="lineno"> 1000</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Matrix&gt;</div>
<div class="line"><a id="l01001" name="l01001"></a><span class="lineno"> 1001</span>    <span class="keywordtype">void</span> optimize(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; svm, Matrix&amp; matrix,RealVector <span class="keyword">const</span>&amp; diagonalModifier, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l01002" name="l01002"></a><span class="lineno"> 1002</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_c_s_v_m_problem.html" title="Problem formulation for binary C-SVM problems.">CSVMProblem&lt;Matrix&gt;</a> SVMProblemType;</div>
<div class="line"><a id="l01003" name="l01003"></a><span class="lineno"> 1003</span>        SVMProblemType svmProblem(matrix,dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(),1e100);</div>
<div class="line"><a id="l01004" name="l01004"></a><span class="lineno"> 1004</span>        <span class="keywordflow">if</span> (this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aee47ba0de2c00b34c32e78ec9751c121">m_trainOffset</a>)</div>
<div class="line"><a id="l01005" name="l01005"></a><span class="lineno"> 1005</span>        {</div>
<div class="line"><a id="l01006" name="l01006"></a><span class="lineno"> 1006</span>            <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_svm_shrinking_problem.html">SvmShrinkingProblem&lt;SVMProblemType&gt;</a> ProblemType;</div>
<div class="line"><a id="l01007" name="l01007"></a><span class="lineno"> 1007</span>            ProblemType problem(svmProblem,<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l01008" name="l01008"></a><span class="lineno"> 1008</span>            <a class="code hl_class" href="classshark_1_1_qp_solver.html" title="Quadratic program solver.">QpSolver&lt; ProblemType &gt;</a> solver(problem);</div>
<div class="line"><a id="l01009" name="l01009"></a><span class="lineno"> 1009</span>            solver.solve(<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">base_type::stoppingCondition</a>(), &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>());</div>
<div class="line"><a id="l01010" name="l01010"></a><span class="lineno"> 1010</span>            column(svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>(),0)= problem.getUnpermutedAlpha();</div>
<div class="line"><a id="l01011" name="l01011"></a><span class="lineno"> 1011</span>            <span class="comment">//compute the bias</span></div>
<div class="line"><a id="l01012" name="l01012"></a><span class="lineno"> 1012</span>            <span class="keywordtype">double</span> sum = 0.0;</div>
<div class="line"><a id="l01013" name="l01013"></a><span class="lineno"> 1013</span>            std::size_t freeVars = 0;</div>
<div class="line"><a id="l01014" name="l01014"></a><span class="lineno"> 1014</span>            <span class="keywordflow">for</span> (std::size_t i=0; i &lt; problem.dimensions(); i++)</div>
<div class="line"><a id="l01015" name="l01015"></a><span class="lineno"> 1015</span>            {</div>
<div class="line"><a id="l01016" name="l01016"></a><span class="lineno"> 1016</span>                <span class="keywordflow">if</span>(problem.alpha(i) &gt; problem.boxMin(i) &amp;&amp; problem.alpha(i) &lt; problem.boxMax(i)){</div>
<div class="line"><a id="l01017" name="l01017"></a><span class="lineno"> 1017</span>                    sum += problem.gradient(i) - problem.alpha(i)*2*diagonalModifier(i);</div>
<div class="line"><a id="l01018" name="l01018"></a><span class="lineno"> 1018</span>                    freeVars++;</div>
<div class="line"><a id="l01019" name="l01019"></a><span class="lineno"> 1019</span>                }</div>
<div class="line"><a id="l01020" name="l01020"></a><span class="lineno"> 1020</span>            }</div>
<div class="line"><a id="l01021" name="l01021"></a><span class="lineno"> 1021</span>            <span class="keywordflow">if</span> (freeVars &gt; 0)</div>
<div class="line"><a id="l01022" name="l01022"></a><span class="lineno"> 1022</span>                svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a1c89cb50933ee211d67af90e6366e0ee">offset</a>(0) =  sum / freeVars;        <span class="comment">//stabilized (averaged) exact value</span></div>
<div class="line"><a id="l01023" name="l01023"></a><span class="lineno"> 1023</span>            <span class="keywordflow">else</span></div>
<div class="line"><a id="l01024" name="l01024"></a><span class="lineno"> 1024</span>                svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a1c89cb50933ee211d67af90e6366e0ee">offset</a>(0) = 0;</div>
<div class="line"><a id="l01025" name="l01025"></a><span class="lineno"> 1025</span>        }</div>
<div class="line"><a id="l01026" name="l01026"></a><span class="lineno"> 1026</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l01027" name="l01027"></a><span class="lineno"> 1027</span>        {</div>
<div class="line"><a id="l01028" name="l01028"></a><span class="lineno"> 1028</span>            <span class="keyword">typedef</span> BoxConstrainedShrinkingProblem&lt;SVMProblemType&gt; ProblemType;</div>
<div class="line"><a id="l01029" name="l01029"></a><span class="lineno"> 1029</span>            ProblemType problem(svmProblem,<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l01030" name="l01030"></a><span class="lineno"> 1030</span>            QpSolver&lt; ProblemType &gt; solver(problem);</div>
<div class="line"><a id="l01031" name="l01031"></a><span class="lineno"> 1031</span>            solver.solve(<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">base_type::stoppingCondition</a>(), &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>());</div>
<div class="line"><a id="l01032" name="l01032"></a><span class="lineno"> 1032</span>            column(svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>(),0) = problem.getUnpermutedAlpha();</div>
<div class="line"><a id="l01033" name="l01033"></a><span class="lineno"> 1033</span>            </div>
<div class="line"><a id="l01034" name="l01034"></a><span class="lineno"> 1034</span>        }</div>
<div class="line"><a id="l01035" name="l01035"></a><span class="lineno"> 1035</span>    }</div>
<div class="line"><a id="l01036" name="l01036"></a><span class="lineno"> 1036</span>};</div>
</div>
<div class="line"><a id="l01037" name="l01037"></a><span class="lineno"> 1037</span> </div>
<div class="line"><a id="l01038" name="l01038"></a><span class="lineno"> 1038</span> </div>
<div class="line"><a id="l01039" name="l01039"></a><span class="lineno"> 1039</span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="foldopen" id="foldopen01040" data-start="{" data-end="};">
<div class="line"><a id="l01040" name="l01040"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html"> 1040</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html">SquaredHingeLinearCSvmTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">AbstractLinearSvmTrainer</a>&lt;InputType&gt;</div>
<div class="line"><a id="l01041" name="l01041"></a><span class="lineno"> 1041</span>{</div>
<div class="line"><a id="l01042" name="l01042"></a><span class="lineno"> 1042</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l01043" name="l01043"></a><span class="lineno"> 1043</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">AbstractLinearSvmTrainer&lt;InputType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">base_type</a>;</div>
<div class="line"><a id="l01044" name="l01044"></a><span class="lineno"> 1044</span> </div>
<div class="foldopen" id="foldopen01045" data-start="{" data-end="}">
<div class="line"><a id="l01045" name="l01045"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html#a2bfdfe17a77da644c389211a1129efff"> 1045</a></span>    <a class="code hl_function" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html#a2bfdfe17a77da644c389211a1129efff">SquaredHingeLinearCSvmTrainer</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">C</a>, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>) </div>
<div class="line"><a id="l01046" name="l01046"></a><span class="lineno"> 1046</span>    : <a class="code hl_class" href="classshark_1_1_abstract_linear_svm_trainer.html" title="Super class of all linear SVM trainers.">AbstractLinearSvmTrainer</a>&lt;<a class="code hl_typedef" href="classshark_1_1_abstract_trainer.html#a0cfa7cdd27b8bb162e64188095f8fa71">InputType</a>&gt;(<a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">C</a>, false, unconstrained){}</div>
</div>
<div class="line"><a id="l01047" name="l01047"></a><span class="lineno"> 1047</span><span class="comment"></span> </div>
<div class="line"><a id="l01048" name="l01048"></a><span class="lineno"> 1048</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen01049" data-start="{" data-end="}">
<div class="line"><a id="l01049" name="l01049"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html#a5bb2c885adf7369a791ac721e0b2bab0"> 1049</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html#a5bb2c885adf7369a791ac721e0b2bab0" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l01050" name="l01050"></a><span class="lineno"> 1050</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;SquaredHingeLinearCSvmTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l01051" name="l01051"></a><span class="lineno"> 1051</span> </div>
<div class="foldopen" id="foldopen01052" data-start="{" data-end="}">
<div class="line"><a id="l01052" name="l01052"></a><span class="lineno"><a class="line" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html#a9c7df5c98dee200e4214abac74ad3bab"> 1052</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_squared_hinge_linear_c_svm_trainer.html#a9c7df5c98dee200e4214abac74ad3bab">train</a>(<a class="code hl_class" href="classshark_1_1_linear_classifier.html" title="Basic linear classifier.">LinearClassifier&lt;InputType&gt;</a>&amp; model, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset)</div>
<div class="line"><a id="l01053" name="l01053"></a><span class="lineno"> 1053</span>    {</div>
<div class="line"><a id="l01054" name="l01054"></a><span class="lineno"> 1054</span>        std::size_t dim = <a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(dataset);</div>
<div class="line"><a id="l01055" name="l01055"></a><span class="lineno"> 1055</span>        <a class="code hl_class" href="classshark_1_1_qp_box_linear.html" title="Quadratic program solver for box-constrained problems with linear kernel.">QpBoxLinear&lt;InputType&gt;</a> solver(dataset, dim);</div>
<div class="line"><a id="l01056" name="l01056"></a><span class="lineno"> 1056</span>        RealMatrix w(1, dim, 0.0);</div>
<div class="line"><a id="l01057" name="l01057"></a><span class="lineno"> 1057</span>        solver.<a class="code hl_function" href="classshark_1_1_qp_box_linear.html#ab8274b4499b2b4c342735a3ab338e2fb" title="Solve the SVM training problem.">solve</a>(</div>
<div class="line"><a id="l01058" name="l01058"></a><span class="lineno"> 1058</span>                1e100,</div>
<div class="line"><a id="l01059" name="l01059"></a><span class="lineno"> 1059</span>                1.0 / <a class="code hl_function" href="classshark_1_1_abstract_linear_svm_trainer.html#ac310ca5530798f1190130d8495dd2f07" title="Return the value of the regularization parameter C.">base_type::C</a>(),</div>
<div class="line"><a id="l01060" name="l01060"></a><span class="lineno"> 1060</span>                <a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">QpConfig::stoppingCondition</a>(),</div>
<div class="line"><a id="l01061" name="l01061"></a><span class="lineno"> 1061</span>                &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">QpConfig::solutionProperties</a>(),</div>
<div class="line"><a id="l01062" name="l01062"></a><span class="lineno"> 1062</span>                <a class="code hl_function" href="classshark_1_1_qp_config.html#a71328214090e442c9fee46103868b0ca" title="Verbosity level of the solver.">QpConfig::verbosity</a>() &gt; 0);</div>
<div class="line"><a id="l01063" name="l01063"></a><span class="lineno"> 1063</span>        row(w,0) = solver.<a class="code hl_function" href="classshark_1_1_qp_box_linear.html#a6db58d9b283d570485001ae03a9b0b87">solutionWeightVector</a>();</div>
<div class="line"><a id="l01064" name="l01064"></a><span class="lineno"> 1064</span>        model.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().setStructure(w);</div>
<div class="line"><a id="l01065" name="l01065"></a><span class="lineno"> 1065</span>    }</div>
</div>
<div class="line"><a id="l01066" name="l01066"></a><span class="lineno"> 1066</span>};</div>
</div>
<div class="line"><a id="l01067" name="l01067"></a><span class="lineno"> 1067</span> </div>
<div class="line"><a id="l01068" name="l01068"></a><span class="lineno"> 1068</span> </div>
<div class="line"><a id="l01069" name="l01069"></a><span class="lineno"> 1069</span>}</div>
<div class="line"><a id="l01070" name="l01070"></a><span class="lineno"> 1070</span><span class="preprocessor">#endif</span></div>
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